Object Detection Using Skewed Polygons Suitable For Parking Space Detection

ABSTRACT

A neural network may be used to determine corner points of a skewed polygon (e.g., as displacement values to anchor box corner points) that accurately delineate a region in an image that defines a parking space. Further, the neural network may output confidence values predicting likelihoods that corner points of an anchor box correspond to an entrance to the parking spot. The confidence values may be used to select a subset of the corner points of the anchor box and/or skewed polygon in order to define the entrance to the parking spot. A minimum aggregate distance between corner points of a skewed polygon predicted using the CNN(s) and ground truth corner points of a parking spot may be used simplify a determination as to whether an anchor box should be used as a positive sample for training.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/819,544, filed on Mar. 16, 2019, which is hereby incorporated byreference in its entirety.

BACKGROUND

Accurate and efficient image processing (e.g., for recognition andclassification) by a machine (e.g., a computer programmed with a trainedneural network) is important in various contexts. For example,autonomous vehicles (e.g., vehicles equipped with advanced driverassistance systems (ADAS)) or drones may analyze image data in real time(e.g., representing images of a roadway and/or a parking lot captured bya camera) to formulate driving operations (e.g., turn steering deviceleft, activate brake system, etc.). In one such instance, a vehicle mayanalyze image data when performing a parking operation in order todetect parking spaces, and to identify properties of the parking spaces,such as location, size, and orientation. To facilitate this process thevehicle may include an object detector that is implemented using aconvolutional neural network (CNN) to detect the existence of parkingspaces in images.

A conventional CNN used to detect parking spaces may use axis-alignedrectangular anchor boxes (all four angles are right angles) as a form ofdetection output. However, parking spaces present in sensor data areoften not rectangular or axis-aligned due to the perspective projectionof the sensor. As such, additional processing is necessary to accuratelyidentify the bounds of each of the parking spaces among the sensor dataonce they are detected. For example, a camera on a vehicle may capturean image of a parking space, and based on the perspective of thecamera's field of view, the parking space may not be depicted in theimage as an axis-aligned rectangle. A conventional CNN may provide anaxis-aligned rectangular anchor box as a form of detection output, inwhich case additional processing is necessary to accurately delineatethe parking space in the image. When training the conventional CNN,positive samples may be identified using an Intersection of Union (IoU)between an anchor box output from the CNN and a ground truth output. TheIoU calculation may be straightforward as the anchor box outputs andground truth are both axis-aligned rectangles.

SUMMARY

The present disclosure relates to object detection using skewed polygons(e.g., quadrilaterals) suitable for parking space detection. Forexample, in some instances at least one Convolutional Neural Network(CNN) may be used to detect and/or delineate one or more parking spacesrepresented in image data. The CNN(s) output may be post-processed andprovided to a downstream system (e.g., vehicle control module) to informsubsequent operations.

Aspects of the disclosure may use a CNN(s) to determine corner points ofa skewed polygon (e.g., as displacement or offset values to anchor shapecorner points) that accurately delineate a region in an image thatdefines a parking space. Furthermore, the disclosure provides for aCNN(s) that outputs confidence values predicting likelihoods that cornerpoints of an anchor shape define or otherwise correspond to an entranceto a parking spot. The confidence values may be used to select a subsetof the corner points of the anchor shape and/or skewed polygon in orderto define the entrance to the parking spot. In accordance withembodiments of the disclosure, the CNN(s) may be used to both predictlikelihoods particular corner points of an anchor shape correspond to anentrance to a parking space along with predicting the displacementvalues to the corner points that delineate the bounds of the parkingspace.

The disclosure further provides for computing a distance (e.g., minimumaggregate distance) between corner points of a skewed polygon predictedusing a CNN(s) and ground truth corner points of a parking spot todetermine whether the anchor shape should be used as a positive samplefor training. For example, a positive sample may be identified based atleast in part on the distance being below a threshold value.

BRIEF DESCRIPTION OF THE DRAWINGS

The present systems and methods for object detection using skewedpolygons suitable for parking space detection is described in detailbelow with reference to the attached drawing figures, which areincorporated herein by reference, wherein:

FIG. 1 is an illustration including an example object detection system,in accordance with some embodiments of the present disclosure;

FIG. 2 is a flow diagram illustrating an example process for identifyingone or more parking spaces, in accordance with some embodiments of thepresent disclosure;

FIG. 3 is an illustration of an image that may be represented by imagedata processed by an object detector, a grid of spatial elements of theobject detector, and a set of anchor shapes that may be associated withone or more of the spatial elements, in accordance with some embodimentsof the present disclosure;

FIG. 4 is an illustration of images overlaid with visual elements fordifferent spatial element resolutions, in accordance with someembodiments of the present disclosure;

FIG. 5A is an illustration that includes a neural network for detectingparking spaces, in accordance with some embodiments of the presentdisclosure;

FIG. 5B is an illustration of an image with an entry-line delineationand a parking space delineation, in accordance with some embodiments ofthe present disclosure;

FIG. 6 is an illustration of an image with ground truth data and cornerpoints of a skewed quadrilateral use for training of an object detector,in accordance with some embodiments of the present disclosure;

FIG. 7 is a block diagram illustrating a method of training a machinelearning model to provide corner points of parking spaces, in accordancewith some embodiments of the present disclosure;

FIG. 8 is a block diagram illustrating a method for determining, using aneural network, corner points of a parking space, in accordance withsome embodiments of the present disclosure;

FIG. 9 is a block diagram illustrating a method of determining, using aneural network, an entrance to a parking space, in accordance with someembodiments of the present disclosure;

FIG. 10 is an illustration of an example operating environment suitablefor use in implementing some embodiments of the present disclosure;

FIG. 11A is an illustration of an example autonomous vehicle, inaccordance with some embodiments of the present disclosure;

FIG. 11B is an example of camera locations and fields of view for theexample autonomous vehicle of FIG. 11A, in accordance with someembodiments of the present disclosure;

FIG. 11C is a block diagram of an example system architecture for theexample autonomous vehicle of FIG. 11A, in accordance with someembodiments of the present disclosure;

FIG. 11D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle of FIG. 11A, in accordancewith some embodiments of the present disclosure; and

FIG. 12 is a block diagram of an example computing device suitable foruse in implementing some embodiments of the present disclosure.

DETAILED DESCRIPTION

The present disclosure relates to object detection using skewed polygons(e.g., quadrilaterals) suitable for parking space detection. Disclosedapproaches may be suitable for driving operations (e.g., autonomousdriving, advanced driver assistance systems (ADAS), etc.) in which aparking space is detected, as well as other applications (e.g.,robotics, video analysis, weather forecasting, medical imaging, etc.)detecting objects (e.g., buildings, windows, doors, driveways,intersections, teeth, real-property tracts, areas or regions ofsurfaces, etc.) corresponding with skewed polygons in image and/orsensor data.

The present disclosure may be described with respect to an exampleautonomous vehicle 1100 (alternatively referred to herein as “vehicle1100” or “autonomous vehicle 1100”), an example of which is described inmore detail herein with respect to FIGS. 11A-11D. Although the presentdisclosure primarily provides examples using autonomous vehicles, othertypes of devices may be used to implement the various approachesdescribed herein, such as robots, unmanned aerial vehicles, camerasystems, weather forecasting devices, medical imaging devices, etc. Inaddition, these approaches may be used for controlling autonomousvehicles, or for other purposes, such as, without limitation, videosurveillance, video or image editing, parking space occupancymonitoring, identification, and/or detection, video or image search orretrieval, object tracking, weather forecasting (e.g., using RADARdata), and/or medical imaging (e.g., using ultrasound or magneticresonance imaging (MRI) data).

While parking spaces are primarily described as the objects beingdetected, disclosed approaches may generally apply to objects that mayappear as skewed polygons (such as quadrilaterals or other shapes) in afield of view of a sensor and/or in image data (e.g., these objects maybe rectangular in the real world but appear as skewed quadrilaterals dueto perspective). While disclosed approaches are described using skewedquadrilaterals and four corner points, disclosed concepts may apply toany number of shapes and points (e.g., corner points) that define thoseshapes. Additionally, while an entrance is primarily defined herein asbeing defined by two of the points (e.g., corner points), in otherexamples an entrance may be defined using any number of points (e.g.,corner points). Further, while the disclosure focuses on objectdetectors implemented using neural networks, in some embodiments othertypes of machine learning models may be employed.

In contrast to conventional approaches, which may use a CNN to predictan axis-aligned rectangular anchor box generally indicating the size andlocation of a parking space, aspects of the disclosure may use a CNN(s)to determine corner points of a skewed quadrilateral (e.g., asdisplacement or offset values to anchor box corner points) thataccurately delineate a region in an image that defines a parking space.As such, in some embodiments, the skewed quadrilateral may be directlyconsumed by downstream systems without requiring additional orsignificant processing to identify the bounds of the parking space. Byreducing subsequent processing, disclosed approaches may be moreefficient and faster than conventional approaches.

Furthermore, in contrast to conventional approaches, the disclosureprovides for a CNN(s) that outputs confidence values predictinglikelihoods that corner points of an anchor box define or otherwisecorrespond to an entrance to a parking spot. The confidence values maybe used to select a subset of the corner points of the anchor box and/orskewed quadrilateral in order to define the entrance to the parkingspot. In accordance with embodiments of the disclosure, processing mayfurther be reduced by using the CNN(s) to both predict likelihoodsparticular corner points of an anchor box correspond to an entrance to aparking space along with predicting the displacement values to thecorner points that delineate the bounds of the parking space.

In another aspect, while a conventional CNN uses Intersection over Union(IoU) to determine whether an axis-aligned rectangular anchor box outputis a positive sample, the disclosure provides for computing a minimumaggregate distance between corner points of a skewed quadrilateralpredicted using a CNN(s) and ground truth corner points of a parkingspot to determine whether the anchor box should be used as a positivesample for training. For example, a positive sample may be identifiedbased at least in part on the minimum aggregate distance (e.g., afternormalization) being below a threshold value. Computing the minimumaggregate distance may be more straightforward than computing an IoU fora skewed quadrilateral, resulting is reduced processing time.

Example Parking Space Detector

Now referring to FIG. 1, FIG. 1 shows an illustration including anexample object detection system 100, in accordance with some embodimentsof the present disclosure. It should be understood that this and otherarrangements described herein are set forth only as examples. Otherarrangements and elements (e.g., machines, interfaces, functions,orders, and groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether forthe sake of clarity. Further, many of the elements described herein arefunctional entities that may be implemented as discrete or distributedcomponents or in conjunction with other components, and in any suitablecombination and location. Various functions described herein as beingperformed by one or more entities may be carried out by hardware,firmware, and/or software. For instance, some functions may be carriedout by a processor executing instructions stored in memory.

In one or more embodiments, the object detection system 100 includes,for example, a communications manager 104, an object detector 106, afeature determiner 108, a confidence score generator 110, a displacementvalue generator 112, a skewed quadrilateral generator 114, and anentrance determiner 126. Some examples described in this disclosure usequadrilaterals (e.g., regular, skewed, irregular, boxes, etc.), and thesystems and methods described may similarly use other polygons.

The communications manager 104 may be configured to managecommunications received by the object detection system 100 (e.g.,comprising sensor data and/or image data) and/or provided by the objectdetection system 100 (e.g., comprising confidence scores, displacementscores, corner points of a skewed quadrilateral, and/or informationderived therefrom). Additionally or alternatively, the communicationsmanager 104 may manage communications within the object detection system100, such as between any of the object detector 106, the Confidencescore generator 110, the displacement value generator 112, the skewedquadrilateral generator 114, the entrance determiner 126, and/or othercomponents that may be included in the object detection system 100 ormay communicate with the object detection system 100, (e.g., downstreamsystem components consuming output from the object detection system100).

With reference to FIG. 2, FIG. 2 is a flow diagram illustrating anexample process 200 for identifying one or more parking spaces, inaccordance with some embodiments of the present disclosure. The objectdetector 106 may be configured to analyze input data, such as sensordata and/or image data representative of any number of parking spaces(or no parking spaces), received from the communications manager 104 andgenerate object detection data that is representative of any number ofdetected objects captured in the input data. To do so, the objectdetector 106 may use the feature determiner 108, the displacement valuegenerator 112, and the confidence score generator 110. The featuredeterminer 108 may be configured to generate or determine features ofthe input data as inputs to the confidence score generator 110 and thedisplacement value generator 112. The confidence score generator 110 maybe configured to generate or determine a confidence score 118 of one ormore anchor boxes based on data from the feature determiner 108. Theconfidence score 118 of each anchor box may predict a likelihood thatthe respective anchor box corresponds to a parking space detected in theinput data.

The displacement value generator 112 may be configured to generate ordetermine displacement values 122 to corner points of each anchor boxbased on data from the feature determiner 108. The skewed quadrilateralgenerator 114 may receive as input, any of the various outputs from theobject detector 106, such as the confidence value 118 and thedisplacement values 122 of each anchor box. The skewed quadrilateralgenerator 114 may generate and/or determine a skewed quadrilateral fromthe input using any suitable technique, such as Non-Maximum Suppression(NMS). This may include the skewed quadrilateral generator 144determining, from any number of anchor boxes corner points of the skewedquadrilateral from the displacement values 122 (e.g., provided by thedisplacement value generator 112) and the corner points of the anchorbox(s). As a non-limiting example, the skewed quadrilateral generator114 may determine which anchor boxes have a confidence value 118exceeding a threshold value (if any). From those anchor boxes, theskewed quadrilateral generator 114 may filter and/or cluster thecandidate detections into one or more output object detections anddetermine corner points of skewed quadrilaterals that correspond tothose output object detections (e.g., using corresponding displacementvalues 122).

In addition to or instead of the confidence score generator 110generating or determining a confidence score 118 predicting a likelihoodthat a respective anchor box corresponds to a parking space detected inthe input data, the confidence score generator 110 may generate ordetermine a confidence score 116 predicting a likelihood that arespective corner point(s) corresponds to a detected entrance to aparking space represented in the input data. The entrance determiner 126may use at least the confidence scores 116 to determine one or moreentrances to one or more parking spaces. As a non-limiting example, theentrance determiner 126 may define an entrance for each object detectionoutput by the skewed quadrilateral generator 114 by selecting a set ofcorner points of each skewed quadrilateral (e.g., two corner points)that have the highest confidence values 116 (e.g., optionally requiringthose confidence values 116 to exceed a threshold value). The selectedcorner points may then be used to define an entrance to thecorresponding parking space (e.g., an entry-line that connects theselected corner points). As indicated by a dashed line in FIG. 2, inother examples the skewed quadrilateral generator 114 may not beimplemented in an object detection system 100 with the entrancedeterminer 126 and/or used by the entrance determiner 126 in order toidentify and/or define entrances to parking spaces or other detectedobject regions.

The object detection system 100 may be implemented in an exampleoperating environment 1000 of FIG. 10, in accordance with someembodiments of the present disclosure. For example, the components ofFIG. 1 may generally be implemented using any combination of a clientdevice(s) 1020, a server device(s) 1060, or a data store(s) 1050. Thus,the object detection system 100 may be provided via multiple devicesarranged in a distributed environment that collectively provide thefunctionality described herein, or may be embodied on a single device(e.g., the vehicle 1100). Thus, while some examples used to describe theobject detection system 100 may refer to particular devices and/orconfigurations, it is contemplated that those examples may be moregenerally applicable to any of the potential combinations of devices andconfigurations described herein. For example, in some embodiments, atleast some of the sensors 1080 used to generate one or more portions ofsensor data input to the object detector 106 may be distributed amongstmultiple vehicles and/or objects in the environment and/or at least oneof the sensors 1080 may be included in the vehicle 1100.

As mentioned herein, the communications manager 104 may be configured tomanage communications received by the object detection system 100 (e.g.,comprising sensor data and/or image data) and/or provided by the objectdetection system 100 (e.g., comprising the confidence scores or values,displacement values, corner points to skewed quadrilaterals, and/orinformation derived therefrom). Additionally or alternatively, thecommunications manager 104 may manage communications within the objectdetection system 100.

Where a communication is received and/or provided as a networkcommunication, the communications manager 104 may comprise a networkinterface which may use one or more wireless antenna(s) (wirelessantenna(s) 1126 of FIG. 11A) and/or modem(s) to communicate over one ormore networks. For example, the network interface may be capable ofcommunication over Long-Term Evolution (LTE), Wideband Code-DivisionMultiple Access (WCDMA), Universal Mobile Telecommunications Service(UMTS), Global System for Mobile communications (GSM), CDMA2000, etc.The network interface may also enable communication between objects inthe environment (e.g., vehicles, mobile devices, etc.), using local areanetwork(s), such as Bluetooth, Bluetooth Low Energy (LE), Z-Wave,ZigBee, etc., and/or Low Power Wide-Area Network(s) (LPWANs), such asLong Range Wide-Area Network (LoRaWAN), SigFox, etc. However, thecommunications manager 104 need not include a network interface, such aswhere the object detection system 100 implemented completely on anautonomous vehicle (e.g., the vehicle 1100). In some examples, one ormore of the communications described herein may be between components ofa computing device 1200 over a bus 1202 of FIG. 12.

Sensor data received by the communications manager 104 may be generatedusing any combination of the sensors 1080 of FIG. 10. For example, thesensor data may include image data representing an image(s), image datarepresenting a video (e.g., snapshots of video), and/or sensor datarepresenting fields of view of sensors (e.g., LIDAR data from LIDARsensor(s) 1164, RADAR data from RADAR sensor(s) 1160, image data from acamera(s) of FIG. 11B, etc.).

The sensor data and/or image data that the communications manager 104provides to the object detector 106 may be generated in a physical orvirtual environment and may include image data representative of afield(s) of view of a camera(s). For example, in aspects of the presentdisclosure, the communications manager 104 provides to the objectdetector 106 image data generated by a camera of the vehicle 1100 in aphysical environment.

While some examples of a machine learning model(s) that may be used forthe object detector 106 and/or other components described herein mayrefer to specific types of machine learning models (e.g., neuralnetworks), it is contemplated that examples of the machine learningmodels described herein may, for example and without limitation, includeany type of machine learning model, such as a machine learning model(s)using linear regression, logistic regression, decision trees, supportvector machines (SVM), Naïve Bayes, k-nearest neighbor (Knn), K meansclustering, random forest, dimensionality reduction algorithms, gradientboosting algorithms, neural networks (e.g., auto-encoders,convolutional, recurrent, perceptrons, Long/Short Term Memory (LSTM),Hopfield, Boltzmann, deep belief, deconvolutional, generativeadversarial, liquid state machine, etc.), and/or other types of machinelearning models.

Referring to FIG. 3, FIG. 3 is an illustration of an image that may berepresented by image data processed by an object detector, a grid ofspatial elements of the object detector, and a set of anchor boxes thatmay be associated with one or more of the spatial elements, inaccordance with some embodiments of the present disclosure. For example,FIG. 3 includes a depiction of an image 310 that may be generated by acamera of the vehicle 1100 in the physical environment and provided tothe object detector 106, which may analyze the image data to generateobject detection data. The object detection data may be representativeof detections, by the object detector 106, of objects in the image 310(which may also be referred to as detected objects). The detectedobjects may or may not correspond to actual objects depicted in theimage 310. For example, some of the detected objects may correspond tofalse detections made by the object detector 106. Further, some of thedetected objects may correspond to the same object depicted in the image310.

The object detector 106 may comprise one or more machine learning modelstrained to generate the object detection data from features extractedfrom the sensor data (e.g., the image data). In some examples, theobject detector 106 is configured to determine a set of object detectiondata (e.g., representing a confidence value and displacement values tocorner points) for each spatial element and/or one or more correspondinganchor boxes thereof for a field of view and/or image. In variousexamples, a spatial element may also refer to a grid cell, an outputcell, a super-pixel, and/or an output pixel of the object detector 106.

In various examples, the spatial elements may form a grid of spatialelement regions. For example, FIG. 3 visually indicates a grid 312 ofspatial elements of the object detector 106 that may be logicallyapplied to sensor data (e.g., representing the image 310). In FIG. 3,the grid 312 is depicted separately from the image 310 so as not toobscure the image 310, and an overlaid depiction 402 is provided in FIG.4. The spatial elements, such as a grid cell 311, may be defined by alocation in the grid. For example, each grid-cell may contain a spatialelement region of a spatial element. In other examples, grid-basedspatial elements may not be used. Further, the spatial elements may notnecessarily define contiguous spatial element regions, may notnecessarily define rectangular-shaped spatial element regions, and/ormay not cover all regions of a field of view and/or image.

In some examples, for a single image or frame (e.g., the image 310), ora set of images or frames, each spatial element of the object detector106 may provide the object detection data for one or more correspondinganchor boxes. In other examples, one or more spatial elements may notprovide object detection data. The object detection data may berepresentative of, for example, the confidence value 118, thedisplacement values 122, and/or the confidence values 116 of each anchorbox of the spatial element, which may or may not correspond to a parkingspace in the field of view and/or the image 310.

FIG. 3 illustrates a set of anchor boxes 314 where each spatial elementapplied to the image 310 may be associated with a corresponding set ofthe anchor boxes 314. Illustrated are eight anchor boxes, but any numberof anchor boxes may be used for a spatial element and anchor boxes fordifferent spatial elements may be different from one another in shape,size, number, etc. The anchor boxes may be various sizes and shapes,such as regular rectangles (e.g., equiangular rectangles); and incontrast to some conventional systems, the anchor boxes may also includeone or more skewed quadrilaterals, such as irregular rectangles (e.g.,no congruent angles); rhombus; kite; trapezoid; parallelogram; isoscelestrapezoid; skewed quadrilateral; or any combination thereof. In FIG. 3,the anchor boxes 314 are depicted separate from the image 310 so as notto obscure the image 310, and an overlaid depiction 402 is provided inFIG. 4.

As described herein, FIG. 4 provides the overlaid depiction 402 in whichthe image 310 is overlaid with the grid 312, and the anchor boxes 314for a single spatial element are positioned at the grid cell 311 itindicate corresponding locations with respect to the image 310. Asdescribed herein, a confidence score(s) and displacement values may begenerated for each anchor box of each grid cell and/or spatial element.For example purposes, the anchor boxes 314 are depicted for only onegrid cell 311, and in other aspects the anchor boxes 314 (or a variationthereof) may be used for multiple grid cells of the grid 312, or eachgrid cell of the grid 312. The anchor boxes 314 for a different gridcell 311 may be at locations corresponding to that grid cell 311 (ormore generally spatial element). The grid 312 is an example of one sizeor resolution of spatial element. As a non-limiting example, the grid312 is 10×6 with 60 grid cells, and as such, if each grid cell isassociated with eight anchor boxes, a confidence score(s) anddisplacement values may be generated for 480 different anchor boxes.

In other aspects, a grid or other arrangement of spatial element regionsmay have a different size or resolution with more spatial regions orfewer spatial regions, in which case the scale of the anchor boxes maybe increased (e.g., with a courser grid with fewer, larger spatialregions) or decreased (e.g., with a finer grid with more, smallerspatial regions). For example, the overlaid depiction 404 includes theimage 310 overlaid with a courser resolution grid 412 (e.g., 2×2) andwith a different set of anchor boxes 414, which may be congruent toanchor boxes 314 (e.g., same shape and size), similar to anchor boxes314 (e.g., same shape and/or different size), or dissimilar to anchorboxes 314 (e.g., different shape and/or different size). In some aspectsof the present disclosure, the object detector may apply multipleresolutions of spatial element regions (e.g., grids) to the same inputdata, each spatial element region corresponding to a respective set ofanchor boxes. Among other potential advantages, using multipleresolutions may improve the likelihood that the object detector 106 isaccurate for both larger parking spaces and smaller parking spaces,whether in the same image (e.g., parking spaces closer to the camera mayappear larger based on the perspective, and parking spaces farther fromthe camera may appear smaller) or different images. In some instances,the actual sets of spatial element regions (e.g., grids) used to analyzeinput data may be significantly finer in resolution than the grids 312and 412. Further, any number of sets of spatial element regions may beemployed.

As described herein, based on the object detection data provided by theobject detector 106 the skewed quadrilateral generator 114 may generateand/or identify one or more skewed quadrilaterals corresponding to oneor more parking spaces and the entrance determiner 126 may determineand/or identify one or more entrances to one or more parking spaces.

Referring to FIG. 5A, FIG. 5A depicts at least a portion of an exampleobject detector 106 implemented using a neural network(s) (e.g., a CNN).For example, the object detector 106 includes a feature backbone network506, such as ResNet 50 or another feature backbone network. In addition,the neural network includes a feature pyramid network 508. Furthermore,the neural network includes a classification sub-network 510.

In embodiments, the feature backbone network 506 and the feature pyramidnetwork 508 may correspond to the feature determiner 108 of FIG. 1, theclassification sub-network 510 may correspond to the confidence scoregenerator 110 of FIG. 1, and the regression sub-network 512 maycorrespond to the displacement value generator 112 of FIG. 1. However,the depiction of the neural network in FIG. 5A is not intended to limitthe object detector 106 to the neural network shown. Additionally, theclassification sub-network 510 is shown as outputting datarepresentative of a confidence score 514 (which may correspond to theconfidence score 118 in FIG. 2). Although not shown for simplicity, inembodiments that detect entrances to parking spaces, the classificationsub-network 510 may additionally or alternatively output datarepresentative of the confidence scores 116 of FIG. 1 or anotherclassification sub-network may be used. The regression sub-network 512is shown as outputting data representative of displacement values 516(which may correspond to the displacement values 122 in FIG. 2). Theoutputs described with respect to the object detector 106 in FIG. 5A maybe provided for each pre-defined anchor box.

In a further aspect of the present disclosure, a skewed quadrilateralgenerator 518, which may correspond to the skewed quadrilateralgenerator 114 in FIG. 1, may generate and/or identify one or more skewedquadrilaterals based on the outputs from the object detector 106. Forexample, based on the displacement values 516 (e.g., Δx₁, Δy₁ . . . ,Δx₄, Δy₄) and the confidence value 514, the skewed quadrilateralgenerator 518 may select the anchor box and adjust the corner positionsor points of the anchor box 522 (e.g., x₁, y₁ . . . , x₄, y₄), togenerate corner points (e.g., adjusted corner points 520 including [x′₁,y′₁ . . . , x′₄, y′₄]) of a skewed quadrilateral. Data representative ofthe skewed quadrilateral (e.g., the adjusted corner points 520) may beprovided to various downstream components or systems. As shown, invarious embodiments, confidence map classification may be performed,such as to classify the anchor box as a positive or negative parkingspace detection (e.g., using a binary classification) and the skewedquadrilateral generator 518 may leverage this information. For example,the object detection system 100 may compare the confidence value 514 ofeach anchor box to a threshold value. A positive detection may resultfor an anchor box when the confidence value 514 is greater than thethreshold value and a negative detection may result when the confidencevalue is less than the threshold value.

As non-limiting examples, the skewed quadrilateral generator 114 maygenerate and/or determine any number of skewed quadrilaterals by formingany number of clusters of detected objects by applying a clusteringalgorithm(s) to the outputs of the object detector 106 for the detectedobjects (e.g., after filtering out negative detections using theconfidence values 514). To cluster detected objects, the skewedquadrilateral generator 114 may cluster the locations of the detectedobjects (e.g., candidate skewed quadrilaterals) together. This may be,for example, based at least in part on the confidence values 514associated with the detected objects and/or other detected object datadescribed herein. In some examples, the skewed quadrilateral generator114 uses a Density-Based Spatial Clustering of Applications with Noise(DBSCAN) algorithm. Other examples include NMS or modified groupRectangles algorithms. A skewed quadrilateral may be selected,determined, and/or generated from each cluster as an output objectdetection (e.g., using one or more algorithms and/or neural networks).

Data representative of the adjusted corner points 520 and/or each skewedquadrilateral determined by the skewed quadrilateral generator 114, maybe provided to various downstream components or systems. For example, inone instance, the corner points of skewed quadrilaterals may be providedto a vehicle control module, which may directly consume the cornerpoints by converting the two-dimensional corner point coordinates tothree-dimensional coordinates or otherwise processing that data, such asto coordinate parking operations of a vehicle. In another aspect, thecorner points of a skewed quadrilateral(s) may be provided to aninstrument cluster control module having a video or image monitor fordisplaying a representation of the one or more parking spaces. Forexample, the corner points may be used to annotate the image 502 and/oran image corresponding or the image 502 with the corner pointsdelineated—e.g., annotated image 525 in FIG. 5A with the delineation(e.g., indicated by dotted lines) 526 of skewed quadrilaterals havingadjusted corner points 520.

In a further aspect, the corner points of the skewed quadrilateral, thedisplacement values 122, and/or confidence values corresponding to thecorner points of the anchor box (e.g., the confidence values 116) may beprovided as input to the entrance determiner 126 to detect and/or defineone or more entrances to one or more parking spaces (e.g., parkingspaces identified by the skewed quadrilateral generator 114). Forexample, an entry line can be detected and/or defined by selecting thetwo corner points (e.g., of the four) with the highest confidence valuesamongst the confidence values 116 of an anchor box. In some examples,the selection may further be based on the confidence values beinggreater than a threshold value (e.g., indicating the corner points areeach likely to correspond to an entrance). The entrance to a parkingspace may be defined as the entry line or otherwise determined and/ordefined using the locations of the selected corner points.

As such, the entrance information determined by the entrance determiner126 may be provided to various downstream components or systems. Forexample, in some instances, the corner points identified ascorresponding to an entrance may be provided to a vehicle controlmodule, which may directly consume the corner points by converting thetwo-dimensional corner point coordinates to three-dimensionalcoordinates or otherwise processing the corner points. In anotheraspect, the corner points may be provided to an instrument clustercontrol module having a video or image monitor for displaying arepresentation of the one or more entrances to one or more parkingspaces. For example, the corner points may be used to annotate the image502 and/or an image corresponding or the image 502 with the cornerpoints and/or entrance delineated—e.g., annotated image 530 in FIG. 5Bmay include the delineation (e.g., indicated by dashed lines) 532 of anentry line to a parking space. Optionally a parking space(s) delineation534 (e.g., dotted line) may also be provided. In one example, adelineation of an entrance and/or a parking space may include acolored-line or other suitable annotation to an image.

Examples of Training a Machine Learning Model(s) for Object Detection

The object detector 106 may be trained using various possibleapproaches. In some examples, the object detector 106 may be trained ina fully supervised manner. Training images together with their labelsmay be grouped in minibatches, where the size of the minibatches may bea tunable hyperparameter. Each minibatch may be passed to an online dataaugmentation layer which may apply transformations to images in thatminibatch. The data augmentation may be used to alleviate possibleoverfitting of the object detector 106 to the training data. The dataaugmentation transformations may include (but are not limited to)spatial transformations such as left-right flipping, zooming-in/-out,random translations, etc., color transformations such as hue, saturationand contrast adjustment, or additive noise. Labels may be transformed toreflect corresponding transformations made to training images.

Augmented images may be passed to the object detector 106 to performforward pass computations. The object detector 106 may perform featureextraction and prediction on a per spatial element basis (e.g.,predictions related to anchor boxes). Loss functions may simultaneouslymeasure the error in the tasks of predicting the various outputs (e.g.,the confidence values and the displacement values for each anchor box).

The component losses for the various outputs may be combined together ina single loss function that applies to the whole minibatch. Then,backward pass computations may take place to recursively computegradients of the cost function with respect to trainable parameters(typically at least the weights and biases of the object detector 106,but not limited to this as there may be other trainable parameters, e.g.when batch normalization is used). Forward and backward passcomputations may typically be handled by a deep learning framework andsoftware stack underneath.

A parameter update for the object detector 106 may then take place. Anoptimizer may be used to make an adjustment to trainable parameters.Examples include stochastic gradient descent, or stochastic gradientdescent with a momentum term. The main hyperparameter connected to theoptimizer may be the learning rate. There may also be otherhyperparameters depending on the optimizer.

Images in the dataset may be presented in a random order for each epochduring training, which may lead to faster convergence. An epoch mayrefer to the number of forward/backward pass iterations used to showeach image of the dataset once to the object detector 106 undertraining. The whole process ‘forward-pass—backward-pass—parameterupdate’ may be iterated until convergence of the trained parameters.Convergence may be assessed by observing the value of the loss functiondecrease to a sufficiently low value on both the training and validationsets, and determining that iterating further would not decrease the lossany further. Other metrics could be used to assess convergence, such asaverage precision computed over a validation set.

During training, validation may be performed periodically, and this mayinvolve checking the average values of the loss function over images ina validation set (separate from the training set). As mentioned herein,each of the outputs of the object detector 106 (e.g., confidencescore(s) of each anchor box, displacement values of each anchor box,etc.) may be associated with a separate loss function used for training.Any suitable loss function(s) may be used.

In accordance with an aspect of the present disclosure, ground-truthdata for a parking space may include corner locations of the parkingspace, and the corner locations may form or define a skewedquadrilateral. Furthermore, positive training samples may be identifiedfrom the outputs of the object detector 106 when skewed quadrilateralcorners of anchor boxes are similar enough to the ground-truth cornerlocations, such as based on matching costs being less than a threshold.In an aspect of the present disclosure, various types of anchor boxesmay be used to train the neural network and identify positive samples.For example, in one aspect, the predefined anchor boxes may includerectangles (e.g., rectangles). Further, the predefined anchor boxes mayinclude rotated rectangles. In addition or instead, one or more of theanchor boxes may include skewed and rotated rectangles. Examples ofskewed rectangles include irregular rectangles (e.g., no congruentangles); rhombus; kite; trapezoid; parallelogram; isosceles trapezoid;skewed quadrilateral; and any combination thereof. Predefined anchorboxes may be manually designed or obtained from ground-truth labelingand may be used to compute ground-truth displacement values used totrain the object detector 106. An anchor box obtained from ground-truthlabeling may be referred to as a “data-driven anchor box,” which isgenerated by clustering or otherwise analyzing ground-truth samples. Forexample, ground-truth samples (e.g., including skewed quadrilaterals)may be generated for one or more images. The ground-truth samples maythen be clustered into one or more clusters, and at least onedata-driven anchor box may be generated, selected, and/or determinedfrom the samples of each cluster of the one or more clusters. In someexamples, a data-driven anchor box may have a shape computed from one ormore of the samples of the cluster (e.g., corresponding to an average orotherwise statistically derived shape of the cluster). In variousexamples, spectral clustering may be executed, such as by computing theaffinity matrix of ground-truth samples using a shape similarityfunction, and performing spectral clustering using the affinity matrixwith k clusters where k is the number of clusters to be generated.

In one aspect the matching cost used to identify a positive sample fromoutput of the object detector 106 is based at least in part on a minimumaggregate distance between the predefined anchor-box corners as adjustedby the corresponding displacement values that are output by the objectdetector 106 and the ground-truth corner locations. This is in contrastto determining positive samples based on intersection of union (IOU) andmay be more straightforward than IOU, since the corner points beingcompared may not define regular rectangles (and instead define skewedquadrilaterals).

A minimum aggregate distance may be computed in various manners. Forexample, referring to FIG. 6, an image 610 is depicted in whichground-truth corner points (B1, B2, B3, and B4) of a depicted parkingspace 602 are shown. The image 610 may be used as a training input tothe object detector 106. As a result, the object detector 106 mayprovide displacement values to corner points of an anchor box that areused to compute adjusted corner points (A1, A2, A3, and A4) of theanchor box, as shown. FIG. 6 shows corner points for only a singleanchor box to simplify this illustration, and in other aspects, similarinformation may be used for each anchor box described herein.

In one aspect of the present disclosure, computing a minimum aggregatedistance includes computing a minimum mean distance. For example, afirst aggregate distance may be computed by determining distancesbetween (A1, B1), (A2, B2), (A3, B3), and (A4, B4), then statisticallyderiving the first aggregate distance from those distances, such asusing a mean. A second, third, and fourth aggregate distance may also becomputed by changing the associations between the corner points of eachdata set (e.g., for each possible combination)—e.g., a second aggregatedistance using (A1, B2), (A2, B3), (A3, B4), and (A4, B1); a thirdaggregate distance using (A1, B3), (A2, B4), (A3, B1), and (A4, B2); anda fourth aggregate distance using (A1, B4), (A2, B1), (A3, B2), and (A4,B3). A minimum aggregate distance may then be selected from among thevarious aggregate distances, and used to determine whether the anchorbox is a positive training sample (e.g., similar to an IOU). Forexample, a positive sample may be selected based at least in part on themean aggregate distance being less than a threshold value. In otheraspects, an average mean distance, or other statistical quantificationmay be selected and used to determine whether a matching cost is lessthan a threshold.

In some aspects of the disclosure, the minimum aggregate distance may bedetermined for any number of anchor boxes associated with the objectdetector 106, to determine whether the anchor box corresponds to apositive sample for training. The confidence values 118 may be used tofilter anchor boxes from consideration as being a positive sample. Forexample, the minimum aggregate distance may be determined for an anchorbox based at least in part on a confidence value 118 that is associatedwith that anchor box. In some examples, the minimum aggregate distancemay be determined for each anchor box having a confidence value 118 thatexceeds a threshold value (e.g., indicating a positive detection).

In a further aspect of the disclosure, the minimum aggregate distancefor each anchor box may be normalized based at least in part on a sizeand/or area defined by the ground-truth corner points (e.g., theground-truth skewed quadrilateral). Normalizing the minimum aggregatedistances may be used to account for size differences between anchorboxes, such as where different anchor box sizes and/or spatial elementregion (e.g., grid) resolutions are employed. In accordance with thedisclosure, an anchor box may be identified as a positive sample whenthe matching cost (e.g., based at least in part on the normalizedminimum aggregate distance) is less than a certain (e.g., predetermined)threshold. Positive samples may then be used to update parameters of theobject detector 106 (e.g., CNN) being trained.

Now referring to FIG. 7, FIG. 7 is a flow diagram showing a method 700for training a machine learning model to provide corner points ofparking spaces, in accordance with some embodiments of the presentdisclosure. Each block of the method 700, and other methods describedherein, comprises a computing process that may be performed using anycombination of hardware, firmware, and/or software. For instance,various functions may be carried out by a processor executinginstructions stored in memory. The method 700 may also be embodied ascomputer-usable instructions stored on computer storage media. Themethod 700 may be provided by a standalone application, a service orhosted service (standalone or in combination with another hostedservice), or a plug-in to another product, to name a few. Methodsdescribed herein may additionally or alternatively be executed by anyone system, or any combination of systems, including, but not limitedto, those described herein and are not limited to particular examples.

The method 700, at block B702, includes applying, to a neural network,image data representative of a parking space. For example, the image 502may be applied to the object detector 106, the image 502 depicting atleast one parking space.

The method 700, at block B704, includes receiving, using the neuralnetwork, data generated from the image data and representative ofdisplacement values to corner points of an anchor shape. For example,the regression sub-network 512 may output the displacement values 516related to the pre-defined anchor box 522 and generated from image datarepresenting the image 502.

The method 700, at block B706, includes determining corner points of askewed polygon from the displacement values to the corner points of theanchor shape. For example, the skewed-quadrilateral generator 518 (orother component used at least for training) may determine the adjustedcorner points 520 of a skewed quadrilateral from the displacement values516 related to the pre-defined anchor box 522.

The method 700, at block B708, includes computing a first distancebetween the corner points of the skewed polygon and ground-truth cornerpoints of the parking space. For example, a minimum aggregate distancemay be computed between (A1, A2, A3, and A4) and (B1, B2, B3, and B4),as described with respect to FIG. 6.

The method 700, at block B710, includes determining a sample ratingbased on the first distance. For example, the sample rating may be theminimum aggregate distance or some derivative thereof (e.g., normalizedbased on ground-truth size).

The method 700, at block B712, includes based on the sample ratingexceeding (e.g., being below) a threshold value, updating parameters ofthe neural network using the anchor shape as a positive training sample.For example, an anchor may be defined as a positive sample when thematching cost (e.g., based on the sample rating) is less than athreshold.

Now referring to FIG. 8, FIG. 8 is a flow diagram showing a method 800for determining, using a neural network, corner points of a parkingspace, in accordance with some embodiments of the present disclosure.The method 800, at block B802, includes applying, to a neural network,sensor data representative of a field of view of at least one sensor inan environment. For example, sensor data representative of the image 502may be applied to the object detector 106, the image representing afield of view of a camera of the vehicle 1100.

The method 800, at block B804, includes receiving, from the neuralnetwork, first data and second data generated from the sensor data, thefirst data representative of displacement values to corner points of ananchor shape and the second data representative of a confidence valuepredicting a likelihood that the anchor shape corresponds to a parkingspace in the field of view of the at least one sensor. For example, theregression sub-network 512 may output data representative of thedisplacement values 516 related to the pre-defined anchor box 522 andgenerated from the sensor data representing the image 502. In addition,the classification sub-network 510 may output data representative of aconfidence score 514 predicting a likelihood that the anchor box 522corresponds to a parking space in the image 502.

The method 800, at block B806, includes based on the confidence valueexceeding a threshold value, determining corner points of a skewedpolygon from the displacement values to the corner points of the anchorshape. For example, the skewed-quadrilateral generator 518 may determinedata representative of the adjusted corner points 520 of a skewedquadrilateral from the displacement values 516 related to thepre-defined anchor box 522 based at least in part on the confidencevalue 514 exceeding a threshold value, as indicated in FIG. 5A.

Now referring to FIG. 9, FIG. 9 is a flow diagram showing a method 900for determining, using a neural network, an entrance to a parking space,in accordance with some embodiments of the present disclosure. Themethod 900, at block B902, includes applying, to a neural network,sensor data representative of a field of view of at least one sensor inan environment. For example, sensor data representative of the image 502may be applied to the object detector 106, the sensor data representinga field of view of a camera of the vehicle 1100.

The method 900, at block B904, includes receiving, from the neuralnetwork, first data and second data generated from the image data. Thefirst data is representative of displacement values to corner points ofan anchor shape, and the second data is representative of confidencevalues predicting likelihoods that the corner points of the anchor shapedefine an entrance to a parking space in the field of view of the atleast one sensor. For example, the regression sub-network 512 may outputdata representative of the displacement values 516 related to thepre-defined anchor box 522 and generated from the sensor data. Inaddition, the classification sub-network 510 (or another similarnetwork) may output the confidence scores 116 of FIG. 2 predictinglikelihood that corner points of the anchor box represent at least aportion of an entrance to a parking space.

The method 900, at block B906, includes selecting a subset of the cornerpoints of the anchor shape based on the confidence values. For example,the entrance determiner 126 may filtered the corner points to determineand/or select the corner points with the highest confidence scores 116.

The method 900, at block B908, includes identifying the entrance to theparking space from the subset of the corner points. For example, oncethe two corner points with the highest confidence scores have beenselected, the entrance determiner 126 may be designated defining anentrance and/or the entry-line for a parking space.

Example Operating Environment

The object detection system 100 and/or the network 502 may beimplemented in an example operating environment 1000 of FIG. 10, inaccordance with some embodiments of the present disclosure.

Among other components not illustrated, the operating environment 1000includes a client device(s) 1020, a network(s) 1040, a server device(s)1060, a sensor(s) 1080, and a data store(s) 1050. It should beunderstood that operating environment 1000 shown in FIG. 10 is anexample of one suitable operating environment. Each of the componentsshown in FIG. 10 may be implemented via any type of computing device,such as one or more of computing device 1200 described in connectionwith FIG. 12, for example. These components may communicate with eachother via the network 1040, which may be wired, wireless, or both. Thenetwork 1040 may include multiple networks, or a network of networks,but is shown in simple form so as not to obscure aspects of the presentdisclosure. By way of example, the network 1040 may include one or morewide area networks (WANs), one or more local area networks (LANs), oneor more public networks such as the Internet, and/or one or more privatenetworks. Where the network 1040 includes a wireless telecommunicationsnetwork, components such as a base station, a communications tower, oreven access points (as well as other components) may provide wirelessconnectivity. In any example, at least one network 1040 may correspondto the network(s) 1190 of FIG. 11D, described further below.

It should be understood that any number of the client devices 1020, theserver devices 1060, the sensors 1080, and the data stores 1050 may beemployed within the operating environment 1000 within the scope of thepresent disclosure. Each may be configured as a single device ormultiple devices cooperating in a distributed environment.

The client device(s) 1020 may include at least some of the components,features, and functionality of the example computing device 1200described herein with respect to FIG. 12. By way of example and notlimitation, a client device 1020 may be embodied as a personal computer(PC), a laptop computer, a mobile device, a smartphone, a tabletcomputer, a smart watch, a wearable computer, a personal digitalassistant (PDA), an MP3 player, a global positioning system (GPS) ordevice, a video player, a handheld communications device, a gamingdevice or system, an entertainment system, a vehicle computer system, anembedded system controller, a remote control, an appliance, a consumerelectronic device, a workstation, any combination of these delineateddevices, or any other suitable device. In any example, at least oneclient device 1020 may be part of a vehicle, such as the vehicle 1100 ofFIGS. 11A-11D, described in further detail herein.

The client device(s) 1020 may include one or more processors, and one ormore computer-readable media. The computer-readable media may includecomputer-readable instructions executable by the one or more processors.The instructions may, when executed by the one or more processors, causethe one or more processors to perform any combination and/or portion ofthe methods described herein and/or implement any portion of thefunctionality of the object detection system 100 of FIG. 1.

The server device(s) 1060 may also include one or more processors, andone or more computer-readable media. The computer-readable mediaincludes computer-readable instructions executable by the one or moreprocessors. The instructions may, when executed by the one or moreprocessors, cause the one or more processors to perform any combinationand/or portion of the methods described herein and/or implement anyportion of the functionality of the object detection system 100 ofFIG. 1. In any example, at least one server device 1060 may correspondto the server(s) 1178 of FIG. 11D, described in further detail herein.

The data store(s) 1050 may comprise one or more computer-readable media.The computer-readable media may include computer-readable instructionsexecutable by the one or more processors. The instructions may, whenexecuted by the one or more processors, cause the one or more processorsto perform any combination and/or portion of the methods describedherein and/or implement any portion of the functionality of the objectdetection system 100 of FIG. 1. The data store(s) 1050 (or computer datastorage) is depicted as a single component, but may be embodied as oneor more data stores (e.g., databases) and may be at least partially inthe cloud. One or more of the data store(s) 1050 may correspond to oneor more of the data stores of FIG. 11C.

Although depicted external to the server device(s) 1060 and the clientdevice(s) 1020, the data store(s) 1050 may be at least partiallyembodied on any combination of the server device(s) 1060 and/or theclient device(s) 1020 (e.g., as memory 1204 (FIG. 12)). For example,some information may be stored on a client device(s) 1020, and otherand/or duplicate information may be stored externally (e.g., on a serverdevice(s) 1060). Thus, it should be appreciated that information in thedata store(s) 1050 may be distributed in any suitable manner across oneor more data stores for storage (which may be hosted externally). Forexample, the data store(s) 1050 may comprise at least some of the one ormore computer-readable media of the server device(s) 1060 and/or atleast some of the one or more computer-readable media of the clientdevice(s) 1020.

The sensor(s) 1080 comprise at least one sensor capable of generatingsensor data representative of at least some aspect of an environment.For example, the sensor(s) 1080 may generate the sensor data 102 of FIG.1A. The sensor(s) 1080 may comprise any combination of a globalnavigation satellite systems (GNSS) sensor(s) (e.g., Global PositioningSystem (GPS) sensor(s)), RADAR sensor(s), ultrasonic sensor(s), LIDARsensor(s), inertial measurement unit (IMU) sensor(s) (e.g.,accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s),etc.), microphone(s), stereo camera(s), wide-view camera(s) (e.g.,fisheye cameras), infrared camera(s), surround camera(s) (e.g., 360degree cameras), long-range and/or mid-range camera(s), speed sensor(s)(e.g., for measuring the speed of the vehicle 1100), vibrationsensor(s), steering sensor(s), brake sensor(s) (e.g., as part of thebrake sensor system), and/or other sensor types.

With reference to FIGS. 11A-11C, the sensor data 102 may be generatedby, for example and without limitation, global navigation satellitesystems (GNSS) sensor(s) 1168 (e.g., Global Positioning Systemsensor(s)), RADAR sensor(s) 1160, ultrasonic sensor(s) 1162, LIDARsensor(s) 1164, inertial measurement unit (IMU) sensor(s) 1166 (e.g.,accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s),etc.), microphone(s) 1196, stereo camera(s) 1168, wide-view camera(s)1170 (e.g., fisheye cameras), infrared camera(s) 1172, surroundcamera(s) 1174 (e.g., 360 degree cameras), long-range and/or mid-rangecamera(s) 1198, speed sensor(s) 1144 (e.g., for measuring the speed ofthe vehicle 1100), vibration sensor(s) 1142, steering sensor(s) 1140,brake sensor(s) (e.g., as part of the brake sensor system 1146), and/orother sensor types.

In some examples, the sensor data 102 may be generated by forward-facingand/or side-facing cameras, such as a wide-view camera(s) 1170, asurround camera(s) 1174, a stereo camera(s) 1168, and/or a long-range ormid-range camera(s) 1198. In some examples, more than one camera orother sensor may be used to incorporate multiple fields of view (e.g.,the field of view of the long-range cameras 1198, the forward-facingstereo camera 1168, and/or the forward facing wide-view camera 1170 ofFIG. 11B).

Example Autonomous Vehicle

FIG. 11A is an illustration of an example autonomous vehicle 1100, inaccordance with some embodiments of the present disclosure. Theautonomous vehicle 1100 (alternatively referred to herein as the“vehicle 1100”) may include a passenger vehicle, such as a car, a truck,a bus, and/or another type of vehicle that accommodates one or morepassengers. Autonomous vehicles are generally described in terms ofautomation levels, defined by the National Highway Traffic SafetyAdministration (NHTSA), a division of the US Department ofTransportation, and the Society of Automotive Engineers (SAE) “Taxonomyand Definitions for Terms Related to Driving Automation Systems forOn-Road Motor Vehicles” (Standard No. J3016-201806, published on Jun.11, 2018, Standard No. J3016-201609, published on Sep. 30, 2016, andprevious and future versions of this standard). The vehicle 1100 may becapable of functionality in accordance with one or more of Level 3-Level5 of the autonomous driving levels. For example, the vehicle 1100 may becapable of conditional automation (Level 3), high automation (Level 4),and/or full automation (Level 5), depending on the embodiment.

The vehicle 1100 may include components such as a chassis, a vehiclebody, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and othercomponents of a vehicle. The vehicle 1100 may include a propulsionsystem 1150, such as an internal combustion engine, hybrid electricpower plant, an all-electric engine, and/or another propulsion systemtype. The propulsion system 1150 may be connected to a drive train ofthe vehicle 1100, which may include a transmission, to enable thepropulsion of the vehicle 1100. The propulsion system 1150 may becontrolled in response to receiving signals from thethrottle/accelerator 1152.

A steering system 1154, which may include a steering wheel, may be usedto steer the vehicle 1100 (e.g., along a desired path or route) when thepropulsion system 1150 is operating (e.g., when the vehicle is inmotion). The steering system 1154 may receive signals from a steeringactuator 1156. The steering wheel may be optional for full automation(Level 5) functionality.

The brake sensor system 1146 may be used to operate the vehicle brakesin response to receiving signals from the brake actuators 1148 and/orbrake sensors.

Controller(s) 1136, which may include one or more system on chips (SoCs)1104 (FIG. 11C) and/or GPU(s), may provide signals (e.g., representativeof commands) to one or more components and/or systems of the vehicle1100. For example, the controller(s) may send signals to operate thevehicle brakes via one or more brake actuators 1148, to operate thesteering system 1154 via one or more steering actuators 1156, to operatethe propulsion system 1150 via one or more throttle/accelerators 1152.The controller(s) 1136 may include one or more onboard (e.g.,integrated) computing devices (e.g., supercomputers) that process sensorsignals, and output operation commands (e.g., signals representingcommands) to enable autonomous driving and/or to assist a human driverin driving the vehicle 1100. The controller(s) 1136 may include a firstcontroller 1136 for autonomous driving functions, a second controller1136 for functional safety functions, a third controller 1136 forartificial intelligence functionality (e.g., computer vision), a fourthcontroller 1136 for infotainment functionality, a fifth controller 1136for redundancy in emergency conditions, and/or other controllers. Insome examples, a single controller 1136 may handle two or more of theabove functionalities, two or more controllers 1136 may handle a singlefunctionality, and/or any combination thereof.

The controller(s) 1136 may provide the signals for controlling one ormore components and/or systems of the vehicle 1100 in response to sensordata received from one or more sensors (e.g., sensor inputs). The sensordata may be received from, for example and without limitation, globalnavigation satellite systems sensor(s) 1158 (e.g., Global PositioningSystem sensor(s)), RADAR sensor(s) 1160, ultrasonic sensor(s) 1162,LIDAR sensor(s) 1164, inertial measurement unit (IMU) sensor(s) 1166(e.g., accelerometer(s), gyroscope(s), magnetic compass(es),magnetometer(s), etc.), microphone(s) 1196, stereo camera(s) 1168,wide-view camera(s) 1170 (e.g., fisheye cameras), infrared camera(s)1172, surround camera(s) 1174 (e.g., 360 degree cameras), long-rangeand/or mid-range camera(s) 1198, speed sensor(s) 1144 (e.g., formeasuring the speed of the vehicle 1100), vibration sensor(s) 1142,steering sensor(s) 1140, brake sensor(s) (e.g., as part of the brakesensor system 1146), and/or other sensor types.

One or more of the controller(s) 1136 may receive inputs (e.g.,represented by input data) from an instrument cluster 1132 of thevehicle 1100 and provide outputs (e.g., represented by output data,display data, etc.) via a human-machine interface (HMI) display 1134, anaudible annunciator, a loudspeaker, and/or via other components of thevehicle 1100. The outputs may include information such as vehiclevelocity, speed, time, map data (e.g., the HD map 1122 of FIG. 11C),location data (e.g., the vehicle's 1100 location, such as on a map),direction, location of other vehicles (e.g., an occupancy grid),information about objects and status of objects as perceived by thecontroller(s) 1136, etc. For example, the HMI display 1134 may displayinformation about the presence of one or more objects (e.g., a streetsign, caution sign, traffic light changing, etc.), and/or informationabout driving maneuvers the vehicle has made, is making, or will make(e.g., changing lanes now, taking exit 34B in two miles, etc.).

The vehicle 1100 further includes a network interface 1124 which may useone or more wireless antenna(s) 1126 and/or modem(s) to communicate overone or more networks. For example, the network interface 1124 may becapable of communication over LTE, WCDMA, UMTS, GSM, CDMA2000, etc. Thewireless antenna(s) 1126 may also enable communication between objectsin the environment (e.g., vehicles, mobile devices, etc.), using localarea network(s), such as Bluetooth, Bluetooth LE, Z-Wave, ZigBee, etc.,and/or low power wide-area network(s) (LPWANs), such as LoRaWAN, SigFox,etc.

FIG. 11B is an example of camera locations and fields of view for theexample autonomous vehicle 1100 of FIG. 11A, in accordance with someembodiments of the present disclosure. The cameras and respective fieldsof view are one example embodiment and are not intended to be limiting.For example, additional and/or alternative cameras may be includedand/or the cameras may be located at different locations on the vehicle1100.

The camera types for the cameras may include, but are not limited to,digital cameras that may be adapted for use with the components and/orsystems of the vehicle 1100. The camera(s) may operate at automotivesafety integrity level (ASIL) B and/or at another ASIL. The camera typesmay be capable of any image capture rate, such as 60 frames per second(fps), 1120 fps, 240 fps, etc., depending on the embodiment. The camerasmay be capable of using rolling shutters, global shutters, another typeof shutter, or a combination thereof. In some examples, the color filterarray may include a red clear clear clear (RCCC) color filter array, ared clear clear blue (RCCB) color filter array, a red blue green clear(RBGC) color filter array, a Foveon X3 color filter array, a Bayersensors (RGGB) color filter array, a monochrome sensor color filterarray, and/or another type of color filter array. In some embodiments,clear pixel cameras, such as cameras with an RCCC, an RCCB, and/or anRBGC color filter array, may be used in an effort to increase lightsensitivity.

In some examples, one or more of the camera(s) may be used to performadvanced driver assistance systems (ADAS) functions (e.g., as part of aredundant or fail-safe design). For example, a Multi-Function MonoCamera may be installed to provide functions including lane departurewarning, traffic sign assist and intelligent headlamp control. One ormore of the camera(s) (e.g., all of the cameras) may record and provideimage data (e.g., video) simultaneously.

One or more of the cameras may be mounted in a mounting assembly, suchas a custom designed (3-D printed) assembly, in order to cut out straylight and reflections from within the car (e.g., reflections from thedashboard reflected in the windshield mirrors) which may interfere withthe camera's image data capture abilities. With reference to wing-mirrormounting assemblies, the wing-mirror assemblies may be custom 3-Dprinted so that the camera mounting plate matches the shape of thewing-mirror. In some examples, the camera(s) may be integrated into thewing-mirror. For side-view cameras, the camera(s) may also be integratedwithin the four pillars at each corner of the cabin.

Cameras with a field of view that include portions of the environment infront of the vehicle 1100 (e.g., front-facing cameras) may be used forsurround view, to help identify forward facing paths and obstacles, aswell aid in, with the help of one or more controllers 1136 and/orcontrol SoCs, providing information critical to generating an occupancygrid and/or determining the preferred vehicle paths. Front-facingcameras may be used to perform many of the same ADAS functions as LIDAR,including emergency braking, pedestrian detection, and collisionavoidance. Front-facing cameras may also be used for ADAS functions andsystems including Lane Departure Warnings (“LDW”), Autonomous CruiseControl (“ACC”), and/or other functions such as traffic signrecognition.

A variety of cameras may be used in a front-facing configuration,including, for example, a monocular camera platform that includes a CMOS(complementary metal oxide semiconductor) color imager. Another examplemay be a wide-view camera(s) 1170 that may be used to perceive objectscoming into view from the periphery (e.g., pedestrians, crossing trafficor bicycles). Although only one wide-view camera is illustrated in FIG.11B, there may any number of wide-view cameras 1170 on the vehicle 1100.In addition, long-range camera(s) 1198 (e.g., a long-view stereo camerapair) may be used for depth-based object detection, especially forobjects for which a neural network has not yet been trained. Thelong-range camera(s) 1198 may also be used for object detection andclassification, as well as basic object tracking.

One or more stereo cameras 1168 may also be included in a front-facingconfiguration. The stereo camera(s) 1168 may include an integratedcontrol unit comprising a scalable processing unit, which may provide aprogrammable logic (FPGA) and a multi-core micro-processor with anintegrated CAN or Ethernet interface on a single chip. Such a unit maybe used to generate a 3-D map of the vehicle's environment, including adistance estimate for all the points in the image. An alternative stereocamera(s) 1168 may include a compact stereo vision sensor(s) that mayinclude two camera lenses (one each on the left and right) and an imageprocessing chip that may measure the distance from the vehicle to thetarget object and use the generated information (e.g., metadata) toactivate the autonomous emergency braking and lane departure warningfunctions. Other types of stereo camera(s) 1168 may be used in additionto, or alternatively from, those described herein.

Cameras with a field of view that include portions of the environment tothe side of the vehicle 1100 (e.g., side-view cameras) may be used forsurround view, providing information used to create and update theoccupancy grid, as well as to generate side impact collision warnings.For example, surround camera(s) 1174 (e.g., four surround cameras 1174as illustrated in FIG. 11B) may be positioned to on the vehicle 1100.The surround camera(s) 1174 may include wide-view camera(s) 1170,fisheye camera(s), 360 degree camera(s), and/or the like. Four example,four fisheye cameras may be positioned on the vehicle's front, rear, andsides. In an alternative arrangement, the vehicle may use three surroundcamera(s) 1174 (e.g., left, right, and rear), and may leverage one ormore other camera(s) (e.g., a forward-facing camera) as a fourthsurround view camera.

Cameras with a field of view that include portions of the environment tothe rear of the vehicle 1100 (e.g., rear-view cameras) may be used forpark assistance, surround view, rear collision warnings, and creatingand updating the occupancy grid. A wide variety of cameras may be usedincluding, but not limited to, cameras that are also suitable as afront-facing camera(s) (e.g., long-range and/or mid-range camera(s)1198, stereo camera(s) 1168), infrared camera(s) 1172, etc.), asdescribed herein.

FIG. 11C is a block diagram of an example system architecture for theexample autonomous vehicle 1100 of FIG. 11A, in accordance with someembodiments of the present disclosure. It should be understood that thisand other arrangements described herein are set forth only as examples.Other arrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory.

Each of the components, features, and systems of the vehicle 1100 inFIG. 11C are illustrated as being connected via bus 1102. The bus 1102may include a Controller Area Network (CAN) data interface(alternatively referred to herein as a “CAN bus”). A CAN may be anetwork inside the vehicle 1100 used to aid in control of variousfeatures and functionality of the vehicle 1100, such as actuation ofbrakes, acceleration, braking, steering, windshield wipers, etc. A CANbus may be configured to have dozens or even hundreds of nodes, eachwith its own unique identifier (e.g., a CAN ID). The CAN bus may be readto find steering wheel angle, ground speed, engine revolutions perminute (RPMs), button positions, and/or other vehicle status indicators.The CAN bus may be ASIL B compliant.

Although the bus 1102 is described herein as being a CAN bus, this isnot intended to be limiting. For example, in addition to, oralternatively from, the CAN bus, FlexRay and/or Ethernet may be used.Additionally, although a single line is used to represent the bus 1102,this is not intended to be limiting. For example, there may be anynumber of busses 1102, which may include one or more CAN busses, one ormore FlexRay busses, one or more Ethernet busses, and/or one or moreother types of busses using a different protocol. In some examples, twoor more busses 1102 may be used to perform different functions, and/ormay be used for redundancy. For example, a first bus 1102 may be usedfor collision avoidance functionality and a second bus 1102 may be usedfor actuation control. In any example, each bus 1102 may communicatewith any of the components of the vehicle 1100, and two or more busses1102 may communicate with the same components. In some examples, eachSoC 1104, each controller 1136, and/or each computer within the vehiclemay have access to the same input data (e.g., inputs from sensors of thevehicle 1100), and may be connected to a common bus, such the CAN bus.

The vehicle 1100 may include one or more controller(s) 1136, such asthose described herein with respect to FIG. 11A. The controller(s) 1136may be used for a variety of functions. The controller(s) 1136 may becoupled to any of the various other components and systems of thevehicle 1100, and may be used for control of the vehicle 1100,artificial intelligence of the vehicle 1100, infotainment for thevehicle 1100, and/or the like.

The vehicle 1100 may include a system(s) on a chip (SoC) 1104. The SoC1104 may include CPU(s) 1106, GPU(s) 1108, processor(s) 1110, cache(s)1112, accelerator(s) 1114, data store(s) 1116, and/or other componentsand features not illustrated. The SoC(s) 1104 may be used to control thevehicle 1100 in a variety of platforms and systems. For example, theSoC(s) 1104 may be combined in a system (e.g., the system of the vehicle1100) with an HD map 1122 which may obtain map refreshes and/or updatesvia a network interface 1124 from one or more servers (e.g., server(s)1178 of FIG. 11D).

The CPU(s) 1106 may include a CPU cluster or CPU complex (alternativelyreferred to herein as a “CCPLEX”). The CPU(s) 1106 may include multiplecores and/or L2 caches. For example, in some embodiments, the CPU(s)1106 may include eight cores in a coherent multi-processorconfiguration. In some embodiments, the CPU(s) 1106 may include fourdual-core clusters where each cluster has a dedicated L2 cache (e.g., a2 MB L2 cache). The CPU(s) 1106 (e.g., the CCPLEX) may be configured tosupport simultaneous cluster operation enabling any combination of theclusters of the CPU(s) 1106 to be active at any given time.

The CPU(s) 1106 may implement power management capabilities that includeone or more of the following features: individual hardware blocks may beclock-gated automatically when idle to save dynamic power; each coreclock may be gated when the core is not actively executing instructionsdue to execution of WFI/WFE instructions; each core may be independentlypower-gated; each core cluster may be independently clock-gated when allcores are clock-gated or power-gated; and/or each core cluster can beindependently power-gated when all cores are power-gated. The CPU(s)1106 may further implement an enhanced algorithm for managing powerstates, where allowed power states and expected wakeup times arespecified, and the hardware/microcode determines the best power state toenter for the core, cluster, and CCPLEX. The processing cores maysupport simplified power state entry sequences in software with the workoffloaded to microcode.

The GPU(s) 1108 may include an integrated GPU (alternatively referred toherein as an “iGPU”). The GPU(s) 1108 may be programmable and may beefficient for parallel workloads. The GPU(s) 1108, in some examples, mayuse an enhanced tensor instruction set. The GPU(s) 1108 may include oneor more streaming microprocessors, where each streaming microprocessormay include an L1 cache (e.g., an L1 cache with at least 96 KB storagecapacity), and two or more of the streaming microprocessors may share anL2 cache (e.g., an L2 cache with a 512 KB storage capacity). In someembodiments, the GPU(s) 1108 may include at least eight streamingmicroprocessors. The GPU(s) 1108 may use compute application programminginterface(s) (API(s)). In addition, the GPU(s) 1108 may use one or moreparallel computing platforms and/or programming models (e.g., NVIDIA'sCUDA).

The GPU(s) 1108 may be power-optimized for best performance inautomotive and embedded use cases. For example, the GPU(s) 1108 may befabricated on a Fin field-effect transistor (FinFET). However, this isnot intended to be limiting and the GPU(s) 1108 may be fabricated usingother semiconductor manufacturing processes. Each streamingmicroprocessor may incorporate a number of mixed-precision processingcores partitioned into multiple blocks. For example, and withoutlimitation, 64 PF32 cores and 32 PF64 cores may be partitioned into fourprocessing blocks. In such an example, each processing block may beallocated 16 FP32 cores, 8 FP64 cores, 16 INT32 cores, twomixed-precision NVIDIA TENSOR COREs for deep learning matrix arithmetic,an L0 instruction cache, a warp scheduler, a dispatch unit, and/or a 64KB register file. In addition, the streaming microprocessors may includeindependent parallel integer and floating-point data paths to providefor efficient execution of workloads with a mix of computation andaddressing calculations. The streaming microprocessors may includeindependent thread scheduling capability to enable finer-grainsynchronization and cooperation between parallel threads. The streamingmicroprocessors may include a combined L1 data cache and shared memoryunit in order to improve performance while simplifying programming.

The GPU(s) 1108 may include a high bandwidth memory (HBM) and/or a 16 GBHBM2 memory subsystem to provide, in some examples, about 900 GB/secondpeak memory bandwidth. In some examples, in addition to, oralternatively from, the HBM memory, a synchronous graphics random-accessmemory (SGRAM) may be used, such as a graphics double data rate typefive synchronous random-access memory (GDDR5).

The GPU(s) 1108 may include unified memory technology including accesscounters to allow for more accurate migration of memory pages to theprocessor that accesses them most frequently, thereby improvingefficiency for memory ranges shared between processors. In someexamples, address translation services (ATS) support may be used toallow the GPU(s) 1108 to access the CPU(s) 1106 page tables directly. Insuch examples, when the GPU(s) 1108 memory management unit (MMU)experiences a miss, an address translation request may be transmitted tothe CPU(s) 1106. In response, the CPU(s) 1106 may look in its pagetables for the virtual-to-physical mapping for the address and transmitsthe translation back to the GPU(s) 1108. As such, unified memorytechnology may allow a single unified virtual address space for memoryof both the CPU(s) 1106 and the GPU(s) 1108, thereby simplifying theGPU(s) 1108 programming and porting of applications to the GPU(s) 1108.

In addition, the GPU(s) 1108 may include an access counter that may keeptrack of the frequency of access of the GPU(s) 1108 to memory of otherprocessors. The access counter may help ensure that memory pages aremoved to the physical memory of the processor that is accessing thepages most frequently.

The SoC(s) 1104 may include any number of cache(s) 1112, including thosedescribed herein. For example, the cache(s) 1112 may include an L3 cachethat is available to both the CPU(s) 1106 and the GPU(s) 1108 (e.g.,that is connected both the CPU(s) 1106 and the GPU(s) 1108). Thecache(s) 1112 may include a write-back cache that may keep track ofstates of lines, such as by using a cache coherence protocol (e.g., MEI,MESI, MSI, etc.). The L3 cache may include 4 MB or more, depending onthe embodiment, although smaller cache sizes may be used.

The SoC(s) 1104 may include one or more accelerators 1114 (e.g.,hardware accelerators, software accelerators, or a combination thereof).For example, the SoC(s) 1104 may include a hardware acceleration clusterthat may include optimized hardware accelerators and/or large on-chipmemory. The large on-chip memory (e.g., 4 MB of SRAM), may enable thehardware acceleration cluster to accelerate neural networks and othercalculations. The hardware acceleration cluster may be used tocomplement the GPU(s) 1108 and to off-load some of the tasks of theGPU(s) 1108 (e.g., to free up more cycles of the GPU(s) 1108 forperforming other tasks). As an example, the accelerator(s) 1114 may beused for targeted workloads (e.g., perception, convolutional neuralnetworks (CNNs), etc.) that are stable enough to be amenable toacceleration. The term “CNN,” as used herein, may include all types ofCNNs, including region-based or regional convolutional neural networks(RCNNs) and Fast RCNNs (e.g., as used for object detection).

The accelerator(s) 1114 (e.g., the hardware acceleration cluster) mayinclude a deep learning accelerator(s) (DLA). The DLA(s) may include oneor more Tensor processing units (TPUs) that may be configured to providean additional ten trillion operations per second for deep learningapplications and inferencing. The TPUs may be accelerators configuredto, and optimized for, performing image processing functions (e.g., forCNNs, RCNNs, etc.). The DLA(s) may further be optimized for a specificset of neural network types and floating point operations, as well asinferencing. The design of the DLA(s) may provide more performance permillimeter than a general-purpose GPU, and vastly exceeds theperformance of a CPU. The TPU(s) may perform several functions,including a single-instance convolution function, supporting, forexample, INT8, INT16, and FP16 data types for both features and weights,as well as post-processor functions.

The DLA(s) may quickly and efficiently execute neural networks,especially CNNs, on processed or unprocessed data for any of a varietyof functions, including, for example and without limitation: a CNN forobject identification and detection using data from camera sensors; aCNN for distance estimation using data from camera sensors; a CNN foremergency vehicle detection and identification and detection using datafrom microphones; a CNN for facial recognition and vehicle owneridentification using data from camera sensors; and/or a CNN for securityand/or safety related events.

The DLA(s) may perform any function of the GPU(s) 1108, and by using aninference accelerator, for example, a designer may target either theDLA(s) or the GPU(s) 1108 for any function. For example, the designermay focus processing of CNNs and floating point operations on the DLA(s)and leave other functions to the GPU(s) 1108 and/or other accelerator(s)1114.

The accelerator(s) 1114 (e.g., the hardware acceleration cluster) mayinclude a programmable vision accelerator(s) (PVA), which mayalternatively be referred to herein as a computer vision accelerator.The PVA(s) may be designed and configured to accelerate computer visionalgorithms for the advanced driver assistance systems (ADAS), autonomousdriving, and/or augmented reality (AR) and/or virtual reality (VR)applications. The PVA(s) may provide a balance between performance andflexibility. For example, each PVA(s) may include, for example andwithout limitation, any number of reduced instruction set computer(RISC) cores, direct memory access (DMA), and/or any number of vectorprocessors.

The RISC cores may interact with image sensors (e.g., the image sensorsof any of the cameras described herein), image signal processor(s),and/or the like. Each of the RISC cores may include any amount ofmemory. The RISC cores may use any of a number of protocols, dependingon the embodiment. In some examples, the RISC cores may execute areal-time operating system (RTOS). The RISC cores may be implementedusing one or more integrated circuit devices, application specificintegrated circuits (ASICs), and/or memory devices. For example, theRISC cores may include an instruction cache and/or a tightly coupledRAM.

The DMA may enable components of the PVA(s) to access the system memoryindependently of the CPU(s) 1106. The DMA may support any number offeatures used to provide optimization to the PVA including, but notlimited to, supporting multi-dimensional addressing and/or circularaddressing. In some examples, the DMA may support up to six or moredimensions of addressing, which may include block width, block height,block depth, horizontal block stepping, vertical block stepping, and/ordepth stepping.

The vector processors may be programmable processors that may bedesigned to efficiently and flexibly execute programming for computervision algorithms and provide signal processing capabilities. In someexamples, the PVA may include a PVA core and two vector processingsubsystem partitions. The PVA core may include a processor subsystem,DMA engine(s) (e.g., two DMA engines), and/or other peripherals. Thevector processing subsystem may operate as the primary processing engineof the PVA, and may include a vector processing unit (VPU), aninstruction cache, and/or vector memory (e.g., VMEM). A VPU core mayinclude a digital signal processor such as, for example, a singleinstruction, multiple data (SIMD), very long instruction word (VLIW)digital signal processor. The combination of the SIMD and VLIW mayenhance throughput and speed.

Each of the vector processors may include an instruction cache and maybe coupled to dedicated memory. As a result, in some examples, each ofthe vector processors may be configured to execute independently of theother vector processors. In other examples, the vector processors thatare included in a particular PVA may be configured to employ dataparallelism. For example, in some embodiments, the plurality of vectorprocessors included in a single PVA may execute the same computer visionalgorithm, but on different regions of an image. In other examples, thevector processors included in a particular PVA may simultaneouslyexecute different computer vision algorithms, on the same image, or evenexecute different algorithms on sequential images or portions of animage. Among other things, any number of PVAs may be included in thehardware acceleration cluster and any number of vector processors may beincluded in each of the PVAs. In addition, the PVA(s) may includeadditional error correcting code (ECC) memory, to enhance overall systemsafety.

The accelerator(s) 1114 (e.g., the hardware acceleration cluster) mayinclude a computer vision network on-chip and SRAM, for providing ahigh-bandwidth, low latency SRAM for the accelerator(s) 1114. In someexamples, the on-chip memory may include at least 4 MB SRAM, consistingof, for example and without limitation, eight field-configurable memoryblocks, that may be accessible by both the PVA and the DLA. Each pair ofmemory blocks may include an advanced peripheral bus (APB) interface,configuration circuitry, a controller, and a multiplexer. Any type ofmemory may be used. The PVA and DLA may access the memory via a backbonethat provides the PVA and DLA with high-speed access to memory. Thebackbone may include a computer vision network on-chip thatinterconnects the PVA and the DLA to the memory (e.g., using the APB).

The computer vision network on-chip may include an interface thatdetermines, before transmission of any control signal/address/data, thatboth the PVA and the DLA provide ready and valid signals. Such aninterface may provide for separate phases and separate channels fortransmitting control signals/addresses/data, as well as burst-typecommunications for continuous data transfer. This type of interface maycomply with ISO 26262 or IEC 612508 standards, although other standardsand protocols may be used.

In some examples, the SoC(s) 1104 may include a real-time ray-tracinghardware accelerator, such as described in U.S. patent application Ser.No. 16/101,1232, filed on Aug. 10, 2018. The real-time ray-tracinghardware accelerator may be used to quickly and efficiently determinethe positions and extents of objects (e.g., within a world model), togenerate real0time visualization simulations, for RADAR signalinterpretation, for sound propagation synthesis and/or analysis, forsimulation of SONAR systems, for general wave propagation simulation,for comparison to LIDAR data for purposes of localization and/or otherfunctions, and/or for other uses.

The accelerator(s) 1114 (e.g., the hardware accelerator cluster) have awide array of uses for autonomous driving. The PVA may be a programmablevision accelerator that may be used for key processing stages in ADASand autonomous vehicles. The PVA's capabilities are a good match foralgorithmic domains needing predictable processing, at low power and lowlatency. In other words, the PVA performs well on semi-dense or denseregular computation, even on small data sets, which need predictablerun-times with low latency and low power. Thus, in the context ofplatforms for autonomous vehicles, the PVAs are designed to run classiccomputer vision algorithms, as they are efficient at object detectionand operating on integer math.

For example, according to one embodiment of the technology, the PVA isused to perform computer stereo vision. A semi-global matching-basedalgorithm may be used in some examples, although this is not intended tobe limiting. Many applications for Level 3-5 autonomous driving requiremotion estimation/stereo matching on-the-fly (e.g., structure frommotion, pedestrian recognition, lane detection, etc.). The PVA mayperform computer stereo vision function on inputs from two monocularcameras.

In some examples, the PVA may be used to perform dense optical flow.According to process raw RADAR data (e.g., using a 4D Fast FourierTransform) to provide Processed RADAR. In other examples, the PVA isused for time of flight depth processing, by processing raw time offlight data to provide processed time of flight data, for example.

The DLA may be used to run any type of network to enhance control anddriving safety, including for example, a neural network that outputs ameasure of confidence for each object detection. Such a confidence valuemay be interpreted as a probability, or as providing a relative “weight”of each detection compared to other detections. This confidence valueenables the system to make further decisions regarding which detectionsshould be considered as true positive detections rather than falsepositive detections. For example, the system may set a threshold valuefor the confidence and consider only the detections exceeding thethreshold value as true positive detections. In an automatic emergencybraking (AEB) system, false positive detections would cause the vehicleto automatically perform emergency braking, which is obviouslyundesirable. Therefore, only the most confident detections should beconsidered as triggers for AEB. The DLA may run a neural network forregressing the confidence value. The neural network may take as itsinput at least some subset of parameters, such as bounding boxdimensions, ground plane estimate obtained (e.g. from anothersubsystem), inertial measurement unit (IMU) sensor 1166 output thatcorrelates with the vehicle 1100 orientation, distance, 3D locationestimates of the object obtained from the neural network and/or othersensors (e.g., LIDAR sensor(s) 1164 or RADAR sensor(s) 1160), amongothers.

The SoC(s) 1104 may include data store(s) 1116 (e.g., memory). The datastore(s) 1116 may be on-chip memory of the SoC(s) 1104, which may storeneural networks to be executed on the GPU and/or the DLA. In someexamples, the data store(s) 1116 may be large enough in capacity tostore multiple instances of neural networks for redundancy and safety.The data store(s) 1112 may comprise L2 or L3 cache(s) 1112. Reference tothe data store(s) 1116 may include reference to the memory associatedwith the PVA, DLA, and/or other accelerator(s) 1114, as describedherein.

The SoC(s) 1104 may include one or more processor(s) 1110 (e.g.,embedded processors). The processor(s) 1110 may include a boot and powermanagement processor that may be a dedicated processor and subsystem tohandle boot power and management functions and related securityenforcement. The boot and power management processor may be a part ofthe SoC(s) 1104 boot sequence and may provide runtime power managementservices. The boot power and management processor may provide clock andvoltage programming, assistance in system low power state transitions,management of SoC(s) 1104 thermals and temperature sensors, and/ormanagement of the SoC(s) 1104 power states. Each temperature sensor maybe implemented as a ring-oscillator whose output frequency isproportional to temperature, and the SoC(s) 1104 may use thering-oscillators to detect temperatures of the CPU(s) 1106, GPU(s) 1108,and/or accelerator(s) 1114. If temperatures are determined to exceed athreshold, the boot and power management processor may enter atemperature fault routine and put the SoC(s) 1104 into a lower powerstate and/or put the vehicle 1100 into a chauffeur to safe stop mode(e.g., bring the vehicle 1100 to a safe stop).

The processor(s) 1110 may further include a set of embedded processorsthat may serve as an audio processing engine. The audio processingengine may be an audio subsystem that enables full hardware support formulti-channel audio over multiple interfaces, and a broad and flexiblerange of audio 110 interfaces. In some examples, the audio processingengine is a dedicated processor core with a digital signal processorwith dedicated RAM.

The processor(s) 1110 may further include an always on processor enginethat may provide necessary hardware features to support low power sensormanagement and wake use cases. The always on processor engine mayinclude a processor core, a tightly coupled RAM, supporting peripherals(e.g., timers and interrupt controllers), various 110 controllerperipherals, and routing logic.

The processor(s) 1110 may further include a safety cluster engine thatincludes a dedicated processor subsystem to handle safety management forautomotive applications. The safety cluster engine may include two ormore processor cores, a tightly coupled RAM, support peripherals (e.g.,timers, an interrupt controller, etc.), and/or routing logic. In asafety mode, the two or more cores may operate in a lockstep mode andfunction as a single core with comparison logic to detect anydifferences between their operations.

The processor(s) 1110 may further include a real-time camera engine thatmay include a dedicated processor subsystem for handling real-timecamera management.

The processor(s) 1110 may further include a high-dynamic range signalprocessor that may include an image signal processor that is a hardwareengine that is part of the camera processing pipeline.

The processor(s) 1110 may include a video image compositor that may be aprocessing block (e.g., implemented on a microprocessor) that implementsvideo post-processing functions needed by a video playback applicationto produce the final image for the player window. The video imagecompositor may perform lens distortion correction on wide-view camera(s)1170, surround camera(s) 1174, and/or on in-cabin monitoring camerasensors. In-cabin monitoring camera sensor is preferably monitored by aneural network running on another instance of the Advanced SoC,configured to identify in cabin events and respond accordingly. Anin-cabin system may perform lip reading to activate cellular service andplace a phone call, dictate emails, change the vehicle's destination,activate or change the vehicle's infotainment system and settings, orprovide voice-activated web surfing. Certain functions are available tothe driver only when the vehicle is operating in an autonomous mode, andare disabled otherwise.

The video image compositor may include enhanced temporal noise reductionfor both spatial and temporal noise reduction. For example, where motionoccurs in a video, the noise reduction weights spatial informationappropriately, decreasing the weight of information provided by adjacentframes. Where an image or portion of an image does not include motion,the temporal noise reduction performed by the video image compositor mayuse information from the previous image to reduce noise in the currentimage.

The video image compositor may also be configured to perform stereorectification on input stereo lens frames. The video image compositormay further be used for user interface composition when the operatingsystem desktop is in use, and the GPU(s) 1108 is not required tocontinuously render new surfaces. Even when the GPU(s) 1108 is poweredon and active doing 3D rendering, the video image compositor may be usedto offload the GPU(s) 1108 to improve performance and responsiveness.

The SoC(s) 1104 may further include a mobile industry processorinterface (MIPI) camera serial interface for receiving video and inputfrom cameras, a high-speed interface, and/or a video input block thatmay be used for camera and related pixel input functions. The SoC(s)1104 may further include an input/output controller(s) that may becontrolled by software and may be used for receiving I/O signals thatare uncommitted to a specific role.

The SoC(s) 1104 may further include a broad range of peripheralinterfaces to enable communication with peripherals, audio codecs, powermanagement, and/or other devices. The SoC(s) 1104 may be used to processdata from cameras (e.g., connected over Gigabit Multimedia Serial Linkand Ethernet), sensors (e.g., LIDAR sensor(s) 1164, RADAR sensor(s)1160, etc. that may be connected over Ethernet), data from bus 1102(e.g., speed of vehicle 1100, steering wheel position, etc.), data fromGNSS sensor(s) 1158 (e.g., connected over Ethernet or CAN bus). TheSoC(s) 1104 may further include dedicated high-performance mass storagecontrollers that may include their own DMA engines, and that may be usedto free the CPU(s) 1106 from routine data management tasks.

The SoC(s) 1104 may be an end-to-end platform with a flexiblearchitecture that spans automation levels 3-5, thereby providing acomprehensive functional safety architecture that leverages and makesefficient use of computer vision and ADAS techniques for diversity andredundancy, provides a platform for a flexible, reliable drivingsoftware stack, along with deep learning tools. The SoC(s) 1104 may befaster, more reliable, and even more energy-efficient andspace-efficient than conventional systems. For example, theaccelerator(s) 1114, when combined with the CPU(s) 1106, the GPU(s)1108, and the data store(s) 1116, may provide for a fast, efficientplatform for level 3-5 autonomous vehicles.

The technology thus provides capabilities and functionality that cannotbe achieved by conventional systems. For example, computer visionalgorithms may be executed on CPUs, which may be configured usinghigh-level programming language, such as the C programming language, toexecute a wide variety of processing algorithms across a wide variety ofvisual data. However, CPUs are oftentimes unable to meet the performancerequirements of many computer vision applications, such as those relatedto execution time and power consumption, for example. In particular,many CPUs are unable to execute complex object detection algorithms inreal-time, which is a requirement of in-vehicle ADAS applications, and arequirement for practical Level 3-5 autonomous vehicles.

In contrast to conventional systems, by providing a CPU complex, GPUcomplex, and a hardware acceleration cluster, the technology describedherein allows for multiple neural networks to be performedsimultaneously and/or sequentially, and for the results to be combinedtogether to enable Level 3-5 autonomous driving functionality. Forexample, a CNN executing on the DLA or dGPU (e.g., the GPU(s) 1120) mayinclude a text and word recognition, allowing the supercomputer to readand understand traffic signs, including signs for which the neuralnetwork has not been specifically trained. The DLA may further include aneural network that is able to identify, interpret, and providessemantic understanding of the sign, and to pass that semanticunderstanding to the path planning modules running on the CPU Complex.

As another example, multiple neural networks may be run simultaneously,as is required for Level 3, 4, or 5 driving. For example, a warning signconsisting of “Caution: flashing lights indicate icy conditions,” alongwith an electric light, may be independently or collectively interpretedby several neural networks. The sign itself may be identified as atraffic sign by a first deployed neural network (e.g., a neural networkthat has been trained), the text “Flashing lights indicate icyconditions” may be interpreted by a second deployed neural network,which informs the vehicle's path planning software (preferably executingon the CPU Complex) that when flashing lights are detected, icyconditions exist. The flashing light may be identified by operating athird deployed neural network over multiple frames, informing thevehicle's path-planning software of the presence (or absence) offlashing lights. All three neural networks may run simultaneously, suchas within the DLA and/or on the GPU(s) 1108.

In some examples, a CNN for facial recognition and vehicle owneridentification may use data from camera sensors to identify the presenceof an authorized driver and/or owner of the vehicle 1100. The always onsensor processing engine may be used to unlock the vehicle when theowner approaches the driver door and turn on the lights, and, insecurity mode, to disable the vehicle when the owner leaves the vehicle.In this way, the SoC(s) 1104 provide for security against theft and/orcarjacking.

In another example, a CNN for emergency vehicle detection andidentification may use data from microphones 1196 to detect and identifyemergency vehicle sirens. In contrast to conventional systems, that usegeneral classifiers to detect sirens and manually extract features, theSoC(s) 1104 use the CNN for classifying environmental and urban sounds,as well as classifying visual data. In a preferred embodiment, the CNNrunning on the DLA is trained to identify the relative closing speed ofthe emergency vehicle (e.g., by using the Doppler effect). The CNN mayalso be trained to identify emergency vehicles specific to the localarea in which the vehicle is operating, as identified by GNSS sensor(s)1158. Thus, for example, when operating in Europe the CNN will seek todetect European sirens, and when in the United States the CNN will seekto identify only North American sirens. Once an emergency vehicle isdetected, a control program may be used to execute an emergency vehiclesafety routine, slowing the vehicle, pulling over to the side of theroad, parking the vehicle, and/or idling the vehicle, with theassistance of ultrasonic sensors 1162, until the emergency vehicle(s)passes.

The vehicle may include a CPU(s) 1118 (e.g., discrete CPU(s), ordCPU(s)), that may be coupled to the SoC(s) 1104 via a high-speedinterconnect (e.g., PCIe). The CPU(s) 1118 may include an X86 processor,for example. The CPU(s) 1118 may be used to perform any of a variety offunctions, including arbitrating potentially inconsistent resultsbetween ADAS sensors and the SoC(s) 1104, and/or monitoring the statusand health of the controller(s) 1136 and/or infotainment SoC 1130, forexample.

The vehicle 1100 may include a GPU(s) 1120 (e.g., discrete GPU(s), ordGPU(s)), that may be coupled to the SoC(s) 1104 via a high-speedinterconnect (e.g., NVIDIA's NVLINK). The GPU(s) 1120 may provideadditional artificial intelligence functionality, such as by executingredundant and/or different neural networks, and may be used to trainand/or update neural networks based at least in part on input (e.g.,sensor data) from sensors of the vehicle 1100.

The vehicle 1100 may further include the network interface 1124 whichmay include one or more wireless antennas 1126 (e.g., one or morewireless antennas for different communication protocols, such as acellular antenna, a Bluetooth antenna, etc.). The network interface 1124may be used to enable wireless connectivity over the Internet with thecloud (e.g., with the server(s) 1178 and/or other network devices), withother vehicles, and/or with computing devices (e.g., client devices ofpassengers). To communicate with other vehicles, a direct link may beestablished between the two vehicles and/or an indirect link may beestablished (e.g., across networks and over the Internet). Direct linksmay be provided using a vehicle-to-vehicle communication link. Thevehicle-to-vehicle communication link may provide the vehicle 1100information about vehicles in proximity to the vehicle 1100 (e.g.,vehicles in front of, on the side of, and/or behind the vehicle 1100).This functionality may be part of a cooperative adaptive cruise controlfunctionality of the vehicle 1100.

The network interface 1124 may include a SoC that provides modulationand demodulation functionality and enables the controller(s) 1136 tocommunicate over wireless networks. The network interface 1124 mayinclude a radio frequency front-end for up-conversion from baseband toradio frequency, and down conversion from radio frequency to baseband.The frequency conversions may be performed through well-known processes,and/or may be performed using super-heterodyne processes. In someexamples, the radio frequency front end functionality may be provided bya separate chip. The network interface may include wirelessfunctionality for communicating over LTE, WCDMA, UMTS, GSM, CDMA2000,Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and/or otherwireless protocols.

The vehicle 1100 may further include data store(s) 1128 which mayinclude off-chip (e.g., off the SoC(s) 1104) storage. The data store(s)1128 may include one or more storage elements including RAM, SRAM, DRAM,VRAM, Flash, hard disks, and/or other components and/or devices that maystore at least one bit of data.

The vehicle 1100 may further include GNSS sensor(s) 1158. The GNSSsensor(s) 1158 (e.g., GPS and/or assisted GPS sensors), to assist inmapping, perception, occupancy grid generation, and/or path planningfunctions. Any number of GNSS sensor(s) 1158 may be used, including, forexample and without limitation, a GPS using a USB connector with anEthernet to Serial (RS-232) bridge.

The vehicle 1100 may further include RADAR sensor(s) 1160. The RADARsensor(s) 1160 may be used by the vehicle 1100 for long-range vehicledetection, even in darkness and/or severe weather conditions. RADARfunctional safety levels may be ASIL B. The RADAR sensor(s) 1160 may usethe CAN and/or the bus 1102 (e.g., to transmit data generated by theRADAR sensor(s) 1160) for control and to access object tracking data,with access to Ethernet to access raw data in some examples. A widevariety of RADAR sensor types may be used. For example, and withoutlimitation, the RADAR sensor(s) 1160 may be suitable for front, rear,and side RADAR use. In some example, Pulse Doppler RADAR sensor(s) areused.

The RADAR sensor(s) 1160 may include different configurations, such aslong range with narrow field of view, short range with wide field ofview, short range side coverage, etc. In some examples, long-range RADARmay be used for adaptive cruise control functionality. The long-rangeRADAR systems may provide a broad field of view realized by two or moreindependent scans, such as within a 250 m range. The RADAR sensor(s)1160 may help in distinguishing between static and moving objects, andmay be used by ADAS systems for emergency brake assist and forwardcollision warning. Long-range RADAR sensors may include monostaticmultimodal RADAR with multiple (e.g., six or more) fixed RADAR antennaeand a high-speed CAN and FlexRay interface. In an example with sixantennae, the central four antennae may create a focused beam pattern,designed to record the vehicle's 1100 surroundings at higher speeds withminimal interference from traffic in adjacent lanes. The other twoantennae may expand the field of view, making it possible to quicklydetect vehicles entering or leaving the vehicle's 1100 lane.

Mid-range RADAR systems may include, as an example, a range of up to1460 m (front) or 80 m (rear), and a field of view of up to 42 degrees(front) or 1450 degrees (rear). Short-range RADAR systems may include,without limitation, RADAR sensors designed to be installed at both endsof the rear bumper. When installed at both ends of the rear bumper, sucha RADAR sensor systems may create two beams that constantly monitor theblind spot in the rear and next to the vehicle.

Short-range RADAR systems may be used in an ADAS system for blind spotdetection and/or lane change assist.

The vehicle 1100 may further include ultrasonic sensor(s) 1162. Theultrasonic sensor(s) 1162, which may be positioned at the front, back,and/or the sides of the vehicle 1100, may be used for park assist and/orto create and update an occupancy grid. A wide variety of ultrasonicsensor(s) 1162 may be used, and different ultrasonic sensor(s) 1162 maybe used for different ranges of detection (e.g., 2.5 m, 4 m). Theultrasonic sensor(s) 1162 may operate at functional safety levels ofASIL B.

The vehicle 1100 may include LIDAR sensor(s) 1164. The LIDAR sensor(s)1164 may be used for object and pedestrian detection, emergency braking,collision avoidance, and/or other functions. The LIDAR sensor(s) 1164may be functional safety level ASIL B. In some examples, the vehicle1100 may include multiple LIDAR sensors 1164 (e.g., two, four, six,etc.) that may use Ethernet (e.g., to provide data to a Gigabit Ethernetswitch).

In some examples, the LIDAR sensor(s) 1164 may be capable of providing alist of objects and their distances for a 360-degree field of view.Commercially available LIDAR sensor(s) 1164 may have an advertised rangeof approximately 1400 m, with an accuracy of 2 cm-3 cm, and with supportfor a 1400 Mbps Ethernet connection, for example. In some examples, oneor more non-protruding LIDAR sensors 1164 may be used. In such examples,the LIDAR sensor(s) 1164 may be implemented as a small device that maybe embedded into the front, rear, sides, and/or corners of the vehicle1100. The LIDAR sensor(s) 1164, in such examples, may provide up to a1420-degree horizontal and 35-degree vertical field-of-view, with a 200m range even for low-reflectivity objects. Front-mounted LIDAR sensor(s)1164 may be configured for a horizontal field of view between 45 degreesand 135 degrees.

In some examples, LIDAR technologies, such as 3D flash LIDAR, may alsobe used. 3D Flash LIDAR uses a flash of a laser as a transmissionsource, to illuminate vehicle surroundings up to approximately 200 m. Aflash LIDAR unit includes a receptor, which records the laser pulsetransit time and the reflected light on each pixel, which in turncorresponds to the range from the vehicle to the objects. Flash LIDARmay allow for highly accurate and distortion-free images of thesurroundings to be generated with every laser flash. In some examples,four flash LIDAR sensors may be deployed, one at each side of thevehicle 1100. Available 3D flash LIDAR systems include a solid-state 3Dstaring array LIDAR camera with no moving parts other than a fan (e.g.,a non-scanning LIDAR device). The flash LIDAR device may use a 5nanosecond class I (eye-safe) laser pulse per frame and may capture thereflected laser light in the form of 3D range point clouds andco-registered intensity data. By using flash LIDAR, and because flashLIDAR is a solid-state device with no moving parts, the LIDAR sensor(s)1164 may be less susceptible to motion blur, vibration, and/or shock.

The vehicle may further include IMU sensor(s) 1166. The IMU sensor(s)1166 may be located at a center of the rear axle of the vehicle 1100, insome examples. The IMU sensor(s) 1166 may include, for example andwithout limitation, an accelerometer(s), a magnetometer(s), agyroscope(s), a magnetic compass(es), and/or other sensor types. In someexamples, such as in six-axis applications, the IMU sensor(s) 1166 mayinclude accelerometers and gyroscopes, while in nine-axis applications,the IMU sensor(s) 1166 may include accelerometers, gyroscopes, andmagnetometers.

In some embodiments, the IMU sensor(s) 1166 may be implemented as aminiature, high performance GPS-Aided Inertial Navigation System(GPS/INS) that combines micro-electro-mechanical systems (MEMS) inertialsensors, a high-sensitivity GPS receiver, and advanced Kalman filteringalgorithms to provide estimates of position, velocity, and attitude. Assuch, in some examples, the IMU sensor(s) 1166 may enable the vehicle1100 to estimate heading without requiring input from a magnetic sensorby directly observing and correlating the changes in velocity from GPSto the IMU sensor(s) 1166. In some examples, the IMU sensor(s) 1166 andthe GNSS sensor(s) 1158 may be combined in a single integrated unit.

The vehicle may include microphone(s) 1196 placed in and/or around thevehicle 1100. The microphone(s) 1196 may be used for emergency vehicledetection and identification, among other things.

The vehicle may further include any number of camera types, includingstereo camera(s) 1168, wide-view camera(s) 1170, infrared camera(s)1172, surround camera(s) 1174, long-range and/or mid-range camera(s)1198, and/or other camera types. The cameras may be used to captureimage data around an entire periphery of the vehicle 1100. The types ofcameras used depends on the embodiments and requirements for the vehicle1100, and any combination of camera types may be used to provide thenecessary coverage around the vehicle 1100. In addition, the number ofcameras may differ depending on the embodiment. For example, the vehiclemay include six cameras, seven cameras, ten cameras, twelve cameras,and/or another number of cameras. The cameras may support, as an exampleand without limitation, Gigabit Multimedia Serial Link (GMSL) and/orGigabit Ethernet. Each of the camera(s) is described with more detailherein with respect to FIG. 11A and FIG. 11B.

The vehicle 1100 may further include vibration sensor(s) 1142. Thevibration sensor(s) 1142 may measure vibrations of components of thevehicle, such as the axle(s). For example, changes in vibrations mayindicate a change in road surfaces. In another example, when two or morevibration sensors 1142 are used, the differences between the vibrationsmay be used to determine friction or slippage of the road surface (e.g.,when the difference in vibration is between a power-driven axle and afreely rotating axle).

The vehicle 1100 may include an ADAS system 1138. The ADAS system 1138may include a SoC, in some examples. The ADAS system 1138 may includeautonomous/adaptive/automatic cruise control (ACC), cooperative adaptivecruise control (CACC), forward crash warning (FCW), automatic emergencybraking (AEB), lane departure warnings (LDW), lane keep assist (LKA),blind spot warning (BSW), rear cross-traffic warning (RCTW), collisionwarning systems (CWS), lane centering (LC), and/or other features andfunctionality.

The ACC systems may use RADAR sensor(s) 1160, LIDAR sensor(s) 1164,and/or a camera(s). The ACC systems may include longitudinal ACC and/orlateral ACC. Longitudinal ACC monitors and controls the distance to thevehicle immediately ahead of the vehicle 1100 and automatically adjustthe vehicle speed to maintain a safe distance from vehicles ahead.Lateral ACC performs distance keeping, and advises the vehicle 1100 tochange lanes when necessary. Lateral ACC is related to other ADASapplications such as LCA and CWS.

CACC uses information from other vehicles that may be received via thenetwork interface 1124 and/or the wireless antenna(s) 1126 from othervehicles via a wireless link, or indirectly, over a network connection(e.g., over the Internet). Direct links may be provided by avehicle-to-vehicle (V2V) communication link, while indirect links may beinfrastructure-to-vehicle (I2V) communication link. In general, the V2Vcommunication concept provides information about the immediatelypreceding vehicles (e.g., vehicles immediately ahead of and in the samelane as the vehicle 1100), while the I2V communication concept providesinformation about traffic further ahead. CACC systems may include eitheror both I2V and V2V information sources. Given the information of thevehicles ahead of the vehicle 1100, CACC may be more reliable and it haspotential to improve traffic flow smoothness and reduce congestion onthe road.

FCW systems are designed to alert the driver to a hazard, so that thedriver may take corrective action. FCW systems use a front-facing cameraand/or RADAR sensor(s) 1160, coupled to a dedicated processor, DSP,FPGA, and/or ASIC, that is electrically coupled to driver feedback, suchas a display, speaker, and/or vibrating component. FCW systems mayprovide a warning, such as in the form of a sound, visual warning,vibration and/or a quick brake pulse.

AEB systems detect an impending forward collision with another vehicleor other object, and may automatically apply the brakes if the driverdoes not take corrective action within a specified time or distanceparameter. AEB systems may use front-facing camera(s) and/or RADARsensor(s) 1160, coupled to a dedicated processor, DSP, FPGA, and/orASIC. When the AEB system detects a hazard, it typically first alertsthe driver to take corrective action to avoid the collision and, if thedriver does not take corrective action, the AEB system may automaticallyapply the brakes in an effort to prevent, or at least mitigate, theimpact of the predicted collision. AEB systems, may include techniquessuch as dynamic brake support and/or crash imminent braking.

LDW systems provide visual, audible, and/or tactile warnings, such assteering wheel or seat vibrations, to alert the driver when the vehicle1100 crosses lane markings. A LDW system does not activate when thedriver indicates an intentional lane departure, by activating a turnsignal. LDW systems may use front-side facing cameras, coupled to adedicated processor, DSP, FPGA, and/or ASIC, that is electricallycoupled to driver feedback, such as a display, speaker, and/or vibratingcomponent.

LKA systems are a variation of LDW systems. LKA systems provide steeringinput or braking to correct the vehicle 1100 if the vehicle 1100 startsto exit the lane.

BSW systems detects and warn the driver of vehicles in an automobile'sblind spot. BSW systems may provide a visual, audible, and/or tactilealert to indicate that merging or changing lanes is unsafe. The systemmay provide an additional warning when the driver uses a turn signal.BSW systems may use rear-side facing camera(s) and/or RADAR sensor(s)1160, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that iselectrically coupled to driver feedback, such as a display, speaker,and/or vibrating component.

RCTW systems may provide visual, audible, and/or tactile notificationwhen an object is detected outside the rear-camera range when thevehicle 1100 is backing up. Some RCTW systems include AEB to ensure thatthe vehicle brakes are applied to avoid a crash. RCTW systems may useone or more rear-facing RADAR sensor(s) 1160, coupled to a dedicatedprocessor, DSP, FPGA, and/or ASIC, that is electrically coupled todriver feedback, such as a display, speaker, and/or vibrating component.

Conventional ADAS systems may be prone to false positive results whichmay be annoying and distracting to a driver, but typically are notcatastrophic, because the ADAS systems alert the driver and allow thedriver to decide whether a safety condition truly exists and actaccordingly. However, in an autonomous vehicle 1100, the vehicle 1100itself must, in the case of conflicting results, decide whether to heedthe result from a primary computer or a secondary computer (e.g., afirst controller 1136 or a second controller 1136). For example, in someembodiments, the ADAS system 1138 may be a backup and/or secondarycomputer for providing perception information to a backup computerrationality module. The backup computer rationality monitor may run aredundant diverse software on hardware components to detect faults inperception and dynamic driving tasks. Outputs from the ADAS system 1138may be provided to a supervisory MCU. If outputs from the primarycomputer and the secondary computer conflict, the supervisory MCU mustdetermine how to reconcile the conflict to ensure safe operation.

In some examples, the primary computer may be configured to provide thesupervisory MCU with a confidence score, indicating the primarycomputer's confidence in the chosen result. If the confidence scoreexceeds a threshold, the supervisory MCU may follow the primarycomputer's direction, regardless of whether the secondary computerprovides a conflicting or inconsistent result. Where the confidencescore does not meet the threshold, and where the primary and secondarycomputer indicate different results (e.g., the conflict), thesupervisory MCU may arbitrate between the computers to determine theappropriate outcome.

The supervisory MCU may be configured to run a neural network(s) that istrained and configured to determine, based at least in part on outputsfrom the primary computer and the secondary computer, conditions underwhich the secondary computer provides false alarms. Thus, the neuralnetwork(s) in the supervisory MCU may learn when the secondarycomputer's output may be trusted, and when it cannot. For example, whenthe secondary computer is a RADAR-based FCW system, a neural network(s)in the supervisory MCU may learn when the FCW system is identifyingmetallic objects that are not, in fact, hazards, such as a drainagegrate or manhole cover that triggers an alarm. Similarly, when thesecondary computer is a camera-based LDW system, a neural network in thesupervisory MCU may learn to override the LDW when bicyclists orpedestrians are present and a lane departure is, in fact, the safestmaneuver. In embodiments that include a neural network(s) running on thesupervisory MCU, the supervisory MCU may include at least one of a DLAor GPU suitable for running the neural network(s) with associatedmemory. In preferred embodiments, the supervisory MCU may compriseand/or be included as a component of the SoC(s) 1104.

In other examples, ADAS system 1138 may include a secondary computerthat performs ADAS functionality using traditional rules of computervision. As such, the secondary computer may use classic computer visionrules (if-then), and the presence of a neural network(s) in thesupervisory MCU may improve reliability, safety and performance. Forexample, the diverse implementation and intentional non-identity makesthe overall system more fault-tolerant, especially to faults caused bysoftware (or software-hardware interface) functionality. For example, ifthere is a software bug or error in the software running on the primarycomputer, and the non-identical software code running on the secondarycomputer provides the same overall result, the supervisory MCU may havegreater confidence that the overall result is correct, and the bug insoftware or hardware on primary computer is not causing material error.

In some examples, the output of the ADAS system 1138 may be fed into theprimary computer's perception block and/or the primary computer'sdynamic driving task block. For example, if the ADAS system 1138indicates a forward crash warning due to an object immediately ahead,the perception block may use this information when identifying objects.In other examples, the secondary computer may have its own neuralnetwork which is trained and thus reduces the risk of false positives,as described herein.

The vehicle 1100 may further include the infotainment SoC 1130 (e.g., anin-vehicle infotainment system (IVI)). Although illustrated anddescribed as a SoC, the infotainment system may not be a SoC, and mayinclude two or more discrete components. The infotainment SoC 1130 mayinclude a combination of hardware and software that may be used toprovide audio (e.g., music, a personal digital assistant, navigationalinstructions, news, radio, etc.), video (e.g., TV, movies, streaming,etc.), phone (e.g., hands-free calling), network connectivity (e.g.,LTE, WiFi, etc.), and/or information services (e.g., navigation systems,rear-parking assistance, a radio data system, vehicle relatedinformation such as fuel level, total distance covered, brake fuellevel, oil level, door open/close, air filter information, etc.) to thevehicle 1100. For example, the infotainment SoC 1130 may radios, diskplayers, navigation systems, video players, USB and Bluetoothconnectivity, carputers, in-car entertainment, WiFi, steering wheelaudio controls, hands free voice control, a heads-up display (HUD), anHMI display 1134, a telematics device, a control panel (e.g., forcontrolling and/or interacting with various components, features, and/orsystems), and/or other components. The infotainment SoC 1130 may furtherbe used to provide information (e.g., visual and/or audible) to auser(s) of the vehicle, such as information from the ADAS system 1138,autonomous driving information such as planned vehicle maneuvers,trajectories, surrounding environment information (e.g., intersectioninformation, vehicle information, road information, etc.), and/or otherinformation.

The infotainment SoC 1130 may include GPU functionality. Theinfotainment SoC 1130 may communicate over the bus 1102 (e.g., CAN bus,Ethernet, etc.) with other devices, systems, and/or components of thevehicle 1100. In some examples, the infotainment SoC 1130 may be coupledto a supervisory MCU such that the GPU of the infotainment system mayperform some self-driving functions in the event that the primarycontroller(s) 1136 (e.g., the primary and/or backup computers of thevehicle 1100) fail. In such an example, the infotainment SoC 1130 mayput the vehicle 1100 into a chauffeur to safe stop mode, as describedherein.

The vehicle 1100 may further include an instrument cluster 1132 (e.g., adigital dash, an electronic instrument cluster, a digital instrumentpanel, etc.). The instrument cluster 1132 may include a controllerand/or supercomputer (e.g., a discrete controller or supercomputer). Theinstrument cluster 1132 may include a set of instrumentation such as aspeedometer, fuel level, oil pressure, tachometer, odometer, turnindicators, gearshift position indicator, seat belt warning light(s),parking-brake warning light(s), engine-malfunction light(s), airbag(SRS) system information, lighting controls, safety system controls,navigation information, etc. In some examples, information may bedisplayed and/or shared among the infotainment SoC 1130 and theinstrument cluster 1132. In other words, the instrument cluster 1132 maybe included as part of the infotainment SoC 1130, or vice versa.

FIG. 11D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle 1100 of FIG. 11A, inaccordance with some embodiments of the present disclosure. The system1176 may include server(s) 1178, network(s) 1190, and vehicles,including the vehicle 1100. The server(s) 1178 may include a pluralityof GPUs 1184(A)-1284(H) (collectively referred to herein as GPUs 1184),PCIe switches 1182(A)-1182(H) (collectively referred to herein as PCIeswitches 1182), and/or CPUs 1180(A)-1180(B) (collectively referred toherein as CPUs 1180). The GPUs 1184, the CPUs 1180, and the PCIeswitches may be interconnected with high-speed interconnects such as,for example and without limitation, NVLink interfaces 1188 developed byNVIDIA and/or PCIe connections 1186. In some examples, the GPUs 1184 areconnected via NVLink and/or NVSwitch SoC and the GPUs 1184 and the PCIeswitches 1182 are connected via PCIe interconnects. Although eight GPUs1184, two CPUs 1180, and two PCIe switches are illustrated, this is notintended to be limiting. Depending on the embodiment, each of theserver(s) 1178 may include any number of GPUs 1184, CPUs 1180, and/orPCIe switches. For example, the server(s) 1178 may each include eight,sixteen, thirty-two, and/or more GPUs 1184.

The server(s) 1178 may receive, over the network(s) 1190 and from thevehicles, image data representative of images showing unexpected orchanged road conditions, such as recently commenced road-work. Theserver(s) 1178 may transmit, over the network(s) 1190 and to thevehicles, neural networks 1192, updated neural networks 1192, and/or mapinformation 1194, including information regarding traffic and roadconditions. The updates to the map information 1194 may include updatesfor the HD map 1122, such as information regarding construction sites,potholes, detours, flooding, and/or other obstructions. In someexamples, the neural networks 1192, the updated neural networks 1192,and/or the map information 1194 may have resulted from new trainingand/or experiences represented in data received from any number ofvehicles in the environment, and/or based at least in part on trainingperformed at a datacenter (e.g., using the server(s) 1178 and/or otherservers).

The server(s) 1178 may be used to train machine learning models (e.g.,neural networks) based at least in part on training data. The trainingdata may be generated by the vehicles, and/or may be generated in asimulation (e.g., using a game engine). In some examples, the trainingdata is tagged (e.g., where the neural network benefits from supervisedlearning) and/or undergoes other pre-processing, while in other examplesthe training data is not tagged and/or pre-processed (e.g., where theneural network does not require supervised learning). Once the machinelearning models are trained, the machine learning models may be used bythe vehicles (e.g., transmitted to the vehicles over the network(s)1190, and/or the machine learning models may be used by the server(s)1178 to remotely monitor the vehicles.

In some examples, the server(s) 1178 may receive data from the vehiclesand apply the data to up-to-date real-time neural networks for real-timeintelligent inferencing. The server(s) 1178 may include deep-learningsupercomputers and/or dedicated AI computers powered by GPU(s) 1184,such as a DGX and DGX Station machines developed by NVIDIA. However, insome examples, the server(s) 1178 may include deep learninginfrastructure that use only CPU-powered datacenters.

The deep-learning infrastructure of the server(s) 1178 may be capable offast, real-time inferencing, and may use that capability to evaluate andverify the health of the processors, software, and/or associatedhardware in the vehicle 1100. For example, the deep-learninginfrastructure may receive periodic updates from the vehicle 1100, suchas a sequence of images and/or objects that the vehicle 1100 has locatedin that sequence of images (e.g., via computer vision and/or othermachine learning object classification techniques). The deep-learninginfrastructure may run its own neural network to identify the objectsand compare them with the objects identified by the vehicle 1100 and, ifthe results do not match and the infrastructure concludes that the AI inthe vehicle 1100 is malfunctioning, the server(s) 1178 may transmit asignal to the vehicle 1100 instructing a fail-safe computer of thevehicle 1100 to assume control, notify the passengers, and complete asafe parking maneuver.

For inferencing, the server(s) 1178 may include the GPU(s) 1184 and oneor more programmable inference accelerators (e.g., NVIDIA's TensorRT 3).The combination of GPU-powered servers and inference acceleration maymake real-time responsiveness possible. In other examples, such as whereperformance is less critical, servers powered by CPUs, FPGAs, and otherprocessors may be used for inferencing.

Example Computing Device

FIG. 12 is a block diagram of an example computing device 1200 suitablefor use in implementing some embodiments of the present disclosure, suchas the object detector 106 and one or more parts of the network 502.Computing device 1200 may include a bus 1202 that directly or indirectlycouples the following devices: memory 1204, one or more centralprocessing units (CPUs) 1206, one or more graphics processing units(GPUs) 1208, a communication interface 1210, input/output (I/O) ports1212, input/output components 1214, a power supply 1216, and one or morepresentation components 1218 (e.g., display(s)).

Although the various blocks of FIG. 12 are shown as connected via thebus 1202 with lines, this is not intended to be limiting and is forclarity only. For example, in some embodiments, a presentation component1218, such as a display device, may be considered an I/O component 1214(e.g., if the display is a touch screen). As another example, the CPUs1206 and/or GPUs 1208 may include memory (e.g., the memory 1204 may berepresentative of a storage device in addition to the memory of the GPUs1208, the CPUs 1206, and/or other components). In other words, thecomputing device of FIG. 12 is merely illustrative. Distinction is notmade between such categories as “workstation,” “server,” “laptop,”“desktop,” “tablet,” “client device,” “mobile device,” “hand-helddevice,” “game console,” “electronic control unit (ECU),” “virtualreality system,” and/or other device or system types, as all arecontemplated within the scope of the computing device of FIG. 12.

The bus 1202 may represent one or more busses, such as an address bus, adata bus, a control bus, or a combination thereof. The bus 1202 mayinclude one or more bus types, such as an industry standard architecture(ISA) bus, an extended industry standard architecture (EISA) bus, avideo electronics standards association (VESA) bus, a peripheralcomponent interconnect (PCI) bus, a peripheral component interconnectexpress (PCIe) bus, and/or another type of bus.

The memory 1204 may include any of a variety of computer-readable media.The computer-readable media may be any available media that may beaccessed by the computing device 1200. The computer-readable media mayinclude both volatile and nonvolatile media, and removable andnon-removable media. By way of example, and not limitation, thecomputer-readable media may comprise computer-storage media andcommunication media.

The computer-storage media may include both volatile and nonvolatilemedia and/or removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, and/or other data types.For example, the memory 1204 may store computer-readable instructions(e.g., that represent a program(s) and/or a program element(s), such asan operating system). Computer-storage media may include, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which may be used to storethe desired information and which may be accessed by computing device1200. As used herein, computer storage media does not comprise signalsper se.

The communication media may embody computer-readable instructions, datastructures, program modules, and/or other data types in a modulated datasignal such as a carrier wave or other transport mechanism and includesany information delivery media. The term “modulated data signal” mayrefer to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal. By wayof example, and not limitation, the communication media may includewired media such as a wired network or direct-wired connection, andwireless media such as acoustic, RF, infrared and other wireless media.Combinations of any of the above should also be included within thescope of computer-readable media.

The CPU(s) 1206 may be configured to execute the computer-readableinstructions to control one or more components of the computing device1200 to perform one or more of the methods and/or processes (e.g.,processes in FIGS. 2, 5A, and 7-9) described herein. The CPU(s) 1206 mayeach include one or more cores (e.g., one, two, four, eight,twenty-eight, seventy-two, etc.) that are capable of handling amultitude of software threads simultaneously. The CPU(s) 1206 mayinclude any type of processor, and may include different types ofprocessors depending on the type of computing device 1200 implemented(e.g., processors with fewer cores for mobile devices and processorswith more cores for servers). For example, depending on the type ofcomputing device 1200, the processor may be an ARM processor implementedusing Reduced Instruction Set Computing (RISC) or an x86 processorimplemented using Complex Instruction Set Computing (CISC). Thecomputing device 1200 may include one or more CPUs 1206 in addition toone or more microprocessors or supplementary co-processors, such as mathco-processors.

The GPU(s) 1208 may be used by the computing device 1200 to rendergraphics (e.g., 3D graphics). The GPU(s) 1208 may include hundreds orthousands of cores that are capable of handling hundreds or thousands ofsoftware threads simultaneously. The GPU(s) 1208 may generate pixel datafor output images in response to rendering commands (e.g., renderingcommands from the CPU(s) 1206 received via a host interface). The GPU(s)1208 may include graphics memory, such as display memory, for storingpixel data. The display memory may be included as part of the memory1204. The GPU(s) 1208 may include two or more GPUs operating in parallel(e.g., via a link). When combined together, each GPU 1208 may generatepixel data for different portions of an output image or for differentoutput images (e.g., a first GPU for a first image and a second GPU fora second image). Each GPU may include its own memory, or may sharememory with other GPUs.

In examples where the computing device 1200 does not include the GPU(s)1208, the CPU(s) 1206 may be used to render graphics.

The communication interface 1210 may include one or more receivers,transmitters, and/or transceivers that enable the computing device 1200to communicate with other computing devices via an electroniccommunication network, included wired and/or wireless communications.The communication interface 1210 may include components andfunctionality to enable communication over any of a number of differentnetworks, such as wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth,Bluetooth LE, ZigBee, etc.), wired networks (e.g., communicating overEthernet), low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.),and/or the Internet.

The I/O ports 1212 may enable the computing device 1200 to be logicallycoupled to other devices including the I/O components 1214, thepresentation component(s) 1218, and/or other components, some of whichmay be built in to (e.g., integrated in) the computing device 1200.Illustrative I/O components 1214 include a microphone, mouse, keyboard,joystick, game pad, game controller, satellite dish, scanner, printer,wireless device, etc. The I/O components 1214 may provide a natural userinterface (NUI) that processes air gestures, voice, or otherphysiological inputs generated by a user. In some instances, inputs maybe transmitted to an appropriate network element for further processing.An NUI may implement any combination of speech recognition, stylusrecognition, facial recognition, biometric recognition, gesturerecognition both on screen and adjacent to the screen, air gestures,head and eye tracking, and touch recognition (as described in moredetail below) associated with a display of the computing device 1200.The computing device 1200 may be include depth cameras, such asstereoscopic camera systems, infrared camera systems, RGB camerasystems, touchscreen technology, and combinations of these, for gesturedetection and recognition. Additionally, the computing device 1200 mayinclude accelerometers or gyroscopes (e.g., as part of an inertiameasurement unit (IMU)) that enable detection of motion. In someexamples, the output of the accelerometers or gyroscopes may be used bythe computing device 1200 to render immersive augmented reality orvirtual reality.

The power supply 1216 may include a hard-wired power supply, a batterypower supply, or a combination thereof. The power supply 1216 mayprovide power to the computing device 1200 to enable the components ofthe computing device 1200 to operate.

The presentation component(s) 1218 may include a display (e.g., amonitor, a touch screen, a television screen, a heads-up-display (HUD),other display types, or a combination thereof), speakers, and/or otherpresentation components. The presentation component(s) 1218 may receivedata from other components (e.g., the GPU(s) 1208, the CPU(s) 1206,etc.), and output the data (e.g., as an image, video, sound, etc.). Inone aspect, the presentation component(s) may display an image (e.g.,525) that delineates a parking space, an entry to a parking space, orany combination thereof.

The disclosure may be described in the general context of computer codeor machine-useable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a personal data assistant or other handhelddevice. Generally, program modules including routines, programs,objects, components, data structures, etc., refer to code that performparticular tasks or implement particular abstract data types. Thedisclosure may be practiced in a variety of system configurations,including hand-held devices, consumer electronics, general-purposecomputers, more specialty computing devices, etc. The disclosure mayalso be practiced in distributed computing environments where tasks areperformed by remote-processing devices that are linked through acommunications network.

As used herein, a recitation of “and/or” with respect to two or moreelements should be interpreted to mean only one element, or acombination of elements. For example, “element A, element B, and/orelement C” may include only element A, only element B, only element C,element A and element B, element A and element C, element B and elementC, or elements A, B, and C. In addition, “at least one of element A orelement B” may include at least one of element A, at least one ofelement B, or at least one of element A and at least one of element B.Further, “at least one of element A and element B” may include at leastone of element A, at least one of element B, or at least one of elementA and at least one of element B.

The subject matter of the present disclosure is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of thisdisclosure. Rather, the inventors have contemplated that the claimedsubject matter might also be embodied in other ways, to includedifferent steps or combinations of steps similar to the ones describedin this document, in conjunction with other present or futuretechnologies. Moreover, although the terms “step” and/or “block” may beused herein to connote different elements of methods employed, the termsshould not be interpreted as implying any particular order among orbetween various steps herein disclosed unless and except when the orderof individual steps is explicitly described.

Claimed are:
 1. A computer-implemented method comprising: applying, to a neural network, image data representative of a parking space; receiving, using the neural network, data generated from the image data and representative of displacement values to corner points of an anchor box; determining corner points of a skewed polygon from the displacement values to the corner points of the anchor box; computing a first distance between the corner points of the skewed polygon and ground-truth corner points of the parking space; determining a sample rating based at least in part on the first distance; and based on the sample rating being below a threshold value, updating parameters of the neural network using the anchor shape as a positive training sample.
 2. The method of claim 1, wherein the first distance comprises a minimum aggregate distance and wherein the sample rating is a normalized version of the minimum aggregate distance.
 3. The method of claim 1, wherein determining the sample rating includes normalizing the first distance based at least in part on an area of a polygon defined by the ground-truth corner points of the parking space.
 4. The method of claim 1, wherein the skewed polygon is a first skewed quadrilateral and the anchor box is a second skewed quadrilateral.
 5. The method of claim 1, wherein the anchor shape is a data-driven anchor box generated from one or more ground-truth samples.
 6. The method of claim 1, wherein the first distance is a minimum mean distance between different combinations of the corner points of the skewed polygon with the ground-truth corner points of the parking space.
 7. The method of claim 1, wherein the corner points of the skewed polygon comprise a first corner (A1), a second corner (A2), a third corner (A3), and a fourth corner (A4); wherein the corner points of the ground-truth corner points of the parking spot comprise a fifth corner (B1), a sixth corner (B2), a seventh corner (B3), and an eighth corner (B4); and wherein computing the first distance comprises computing a first normalized aggregate distance from distances (A1, B1), (A2, B2), (A3, B3), and (A4, B4); a second normalized aggregate distance from distances (A1, B2), (A2, B3), (A3, B4), and (A4, B1); a third normalized aggregate distance from distances (A1, B3), (A2, B4), (A3, B1), and (A4, B2); and a fourth normalized aggregate distance from distances (A1, B4), (A2, B1), (A3, B2), and (A4, B3), and the first distance is a smallest of the first normalized aggregate distance, the second normalized aggregate distance, the third normalized aggregate distance, and the fourth normalized aggregate distance.
 8. A computer-implemented method comprising: applying, to a neural network, sensor data representative of a field of view of at least one sensor in an environment; receiving, from the neural network, first data and second data generated from the sensor data, the first data representative of displacement values to corner points of an anchor shape and the second data representative of a confidence value predicting a likelihood that the anchor shape corresponds to a parking space in the field of view of the at least one sensor; and based at least in part on the confidence value exceeding a threshold value, determining corner points of a skewed polygon that corresponds to the displacement values to the corner points of the anchor shape.
 9. The method of claim 8, wherein the anchor shape is of a plurality of anchor shapes associated with a spatial element of the neural network, and the neural network outputs for each given anchor shape of the plurality of anchor shapes data representative of displacement values to corner points of the given anchor shape and a confidence value predicting a corresponding likelihood that the given anchor shape corresponds to a corresponding parking space in the field of view of the at least one sensor.
 10. The method of claim 8, wherein the anchor shape is of a plurality of anchor shapes associated with a grid of spatial elements of the neural network, and the neural network outputs, for each given anchor shape of the plurality of anchor shapes, data representative of displacement values to corner points of the given anchor shape and a confidence value predicting a corresponding likelihood that the given anchor shape corresponds to a corresponding parking space in the field of view of the at least one sensor.
 11. The method of claim 8, wherein the sensor data comprises image data representative of a field of view of a camera.
 12. The method of claim 8, wherein the anchor shape is of a plurality of anchor shapes associated with one or more spatial elements of the neural network, and the plurality of anchor shapes comprise different shapes of skewed polygons.
 13. The method of claim 8, wherein the skewed polygon is a first skewed quadrilateral and the anchor shape is a second skewed quadrilateral.
 14. The method of claim 8 further comprising: receiving, from the neural network, third data representative of confidence values predicting likelihoods that the corner points of the skewed polygon define an entrance to the parking space in the field of view of the at least one sensor; selecting a subset of the corner points of the anchor shape based at least in part on the confidence values; and identifying the entrance to the parking space from the subset of the corner points.
 15. The method of claim 8, further comprising controlling one or more operations of an autonomous vehicle based at least in part on the corner points of the skewed polygon.
 16. A computer-implemented method comprising: applying, to a neural network, sensor data representative of a field of view of at least one sensor in an environment; receiving, from the neural network, first data and second data generated from the image data, the first data representative of displacement values to corner points of an anchor shape and the second data representative of confidence values predicting likelihoods that the corner points of the anchor shape define an entrance to a parking spot in the field of view of the at least one sensor; selecting a subset of the corner points of the anchor shape based on the confidence values; and identifying the entrance to the parking spot from the subset of the corner points.
 17. The computer-implemented method of claim 16 further comprising: determining corner points of a skewed polygon from the displacement values to the corner points of the anchor shape; and controlling one or more operations of an autonomous vehicle based at least in part on the corner points of the skewed polygon and the entrance to the parking spot.
 18. The method of claim 16, wherein the anchor shape is a skewed polygon.
 19. The method of claim 16, wherein the anchor shape is of a plurality of anchor shapes associated with a spatial element of the neural network, and the neural network outputs for each given anchor shape of the plurality of anchor shapes data representative of displacement values to corner points of the given anchor shape and confidence values predicting corresponding likelihoods that the corner points of the given anchor shape define a given entrance to a corresponding parking spot in the field of view of the at least one sensor.
 20. The method of claim 16, wherein the anchor shape is of a plurality of anchor shapes associated with a grid of spatial elements of the neural network, and the neural network outputs for each given anchor shape of the plurality of anchor shapes data representative of displacement values to corner points of the given anchor shape and confidence values predicting corresponding likelihoods that the corner points of the given anchor shape define a given entrance to a corresponding parking spot in the field of view of the at least one sensor. 